Integrated functional and molecular profiling of cells

ABSTRACT

Presented herein are methods of evaluating cellular activity by: placing a cell population on an area; assaying for a dynamic behavior of the cell population as a function of time; identifying cell(s) of interest based on the dynamic behavior; characterizing a molecular profile of the cell(s); and correlating the obtained information. The assayed dynamic behavior can include cellular activation, cellular inhibition, cellular interaction, protein expression, protein secretion, cellular proliferation, changes in cellular morphology, motility, cell death, cell cytotoxicity, cell lysis, and combinations thereof. Sensors associated with the area may be utilized to facilitate assaying. Molecular profiles of the cell(s) can then be characterized by various methods, such as DNA analysis, RNA analysis, and protein analysis. The dynamic behavior and molecular profile can then be correlated for various purposes, such as predicting clinical outcome of a treatment, screening cells, facilitating a treatment, diagnosing a disease, and monitoring cellular activity.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationNo. 62/138,813, filed on Mar. 26, 2015; and U.S. Provisional PatentApplication No. 62/157,174, filed on May 5, 2015. The entirety of eachof the aforementioned applications is incorporated herein by reference.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH

This invention was made with government support under RO1 Grant No.CA174385, awarded by the National Institutes of Health (NIH); and GrantNo. RP130570, awarded by the Cancer Prevention and Research Institute ofTexas (CPRIT). The government has certain rights in the invention.

BACKGROUND

Current methods of studying cellular activity lack the ability tointegrate dynamic cellular behavior with molecular behavior at thesingle-cell level. The present disclosure addresses the aforementioneddeficiency in the art.

SUMMARY

In some embodiments, the present disclosure pertains to methods ofevaluating cellular activity by: (a) placing a cell population on anarea; (b) assaying for a dynamic behavior of the cell population as afunction of time; (c) identifying one or more cells of interest based onthe dynamic behavior; (d) characterizing a molecular profile of the oneor more identified cells; and (e) correlating the information obtainedfrom steps (b) and (d). In some embodiments, the methods of the presentdisclosure also include a step of obtaining the cell population from asource, such as a tissue or a blood sample.

In some embodiments, the cell population includes immune cells. In someembodiments, the cell population includes, without limitation, T cells,B cells, monocytes, macrophages, neutrophils, dendritic cells, naturalkiller cells, fibroblasts, stromal cells, stem cells, progenitor cells,tumor cells, tumor stem cells, tumor infiltrating lymphocytes, andcombinations thereof. In some embodiments the cell population includes Tcells.

In some embodiments, the cell population is placed on an area asindividual cells. In some embodiments the area includes a plurality ofcontainers. In some embodiments the containers are in the form of atleast one of wells, channels, compartments, and combinations thereof. Insome embodiments, the containers are in the form of an array ofnanowells.

In some embodiments, the dynamic behavior to be assayed includes,without limitation, cellular activation, cellular inhibition, cellularinteraction, protein expression, protein secretion, metabolitesecretion, changes in lipid profiles, microvesicle secretion, exosomesecretion, microparticle secretion, changes in cellular mass, cellularproliferation, changes in cellular morphology, motility, cell death,cell cytotoxicity, cell lysis, cell membrane polarization, establishmentof a synapse, dynamic trafficking of proteins, granule polarization,calcium activation, metabolic changes, small molecule secretion, protonsecretion, and combinations thereof. In some embodiments, the assayingoccurs by visualizing the dynamic behavior by various methods, such astime-lapse imaging microscopy.

For instance, in some embodiments, the motility of a cell population isassayed by evaluating at least one of cellular location, cellularmovements, cellular displacement, cellular speed, cellular movementpaths on the area, cellular infiltration, cellular trafficking, andcombinations thereof. In some embodiments, the cell cytotoxicity of acell population is assayed by evaluating release of cytotoxic moleculesfrom the cell population. In some embodiments, the cellular interactionof a cell population is assayed by evaluating duration of cellularinteractions, number of cellular interactions, calcium activation,granule polarization, protein localization, motility during cellularinteraction, termination of cellular interaction, and combinationsthereof.

In some embodiments, the assaying includes the use of a sensorassociated with an area. In additional embodiments, the presentdisclosure pertains to methods of evaluating cellular activity by: (a)placing a cell population on an area that is associated with a sensor;and (b) assaying for a dynamic behavior of the cell population as afunction of time.

In some embodiments, the sensor is in the form of a bead. In someembodiments, the bead includes diameters that range from about 3 μm toabout 5 μm. In some embodiments, the sensor includes an analyte bindingagent that is directed against an analyte of interest (e.g., secretedproteins, cell lysate components, cellular receptors, and combinationsthereof).

In some embodiments, the sensor is utilized to assay the dynamicbehavior of a single cell in the cell population in real-time. Forinstance, in some embodiments, protein expression is assayed by thesensors of the present disclosure through capture of cell lysatecomponents. In some embodiments, protein secretion is assayed by thesensors of the present disclosure through capture of secreted proteins.In some embodiments, the sensors of the present disclosure are utilizedas a fiduciary marker to enable auto-focusing of the cell populationduring the assaying. In some embodiments the cell population is lysedprior to incubation with the sensors.

Various methods may also be utilized to identify one or more cells ofinterest based on an assayed dynamic cellular behavior. For instance, insome embodiments, one or more cells are identified automatically throughthe use of algorithms. Thereafter, various molecular profiles of theidentified cells can be characterized.

In some embodiments, the characterized molecular profiles can include,without limitation, transcription activity, transcriptomic profile, geneexpression activity, genomic profile, protein expression activity,proteomic profile, protein interaction activity, cellular receptorexpression activity, lipid profile, lipid activity, carbohydrateprofile, microvesicle activity, glucose activity, metabolic profile, andcombinations thereof. In some embodiments, the characterizing occurs bya method that includes, without limitation, DNA analysis, RNA analysis,protein analysis, lipid analysis, metabolite analysis, massspectrometry, and combinations thereof.

Various methods may also be utilized to correlate the obtainedinformation. For instance, in some embodiments the correlating includesintegrating the assayed dynamic behavior and the characterized molecularprofile. In some embodiments, the correlating includes correlating themotility of the one or more identified cells to gene expression ortranscription activities of the one or more identified cells. In someembodiments, the correlating includes correlating the motility of theone or more identified cells to protein interaction activity of the oneor more identified cells. In some embodiments, the correlating includescorrelating the cellular interaction activity of the one or moreidentified cells to protein expression activity of the one or moreidentified cells.

The correlated information may be utilized for various purposes. Forinstance, in some embodiments, the correlated information can beutilized for at least one of predicting clinical outcome of a treatment(e.g., immunotherapy), screening cells (e.g., multi-killer T cells),retrieving cells (e.g., by micromanipulation) for further evaluation(e.g., further study or expansion), facilitating a treatment, diagnosinga disease, monitoring cellular activity, and combinations thereof.

DESCRIPTION OF THE FIGURES

FIG. 1 illustrates a scheme of a method of evaluating cellular activity.

FIG. 2 illustrates a small dataset based on a time-lapse imagingmicroscopy in Nanowell Grids (TIMING) assay. FIG. 2A shows an array of168×70 nanowells arranged in blocks of 7×7 (with 5×5 usable innernanowells/block). FIG. 2B shows enlarged views of five blockshighlighted by the red box in Panel A. FIG. 2C shows time-series data at5-minute intervals for the nanowell highlighted by the red box in PanelB. FIG. 2D shows enlargement of a single frame of the time-lapse seriesfor the nanowell shown in Panel C, showing 3 NALM-6 target cells(green), one CD19-specific CAR⁺ T-cell (red, lower right) in contactwith a target cell (contact region appears yellow), and a redfluorescent debris particle that is rejected in the analysis (leftedge).

FIG. 3 illustrates automated image analysis challenges. FIGS. 3A-H showcample image frames. The red arrows indicate unclear boundaries betweenadjacent cells. The yellow arrows highlight low-intensity cells that aredifficult to detect. The green arrows highlight cells that are difficultto segment due to non-uniform fluorescence. FIGS. 3A, 3B, 3D, 3E, and 3Fexemplify frames with low contrast and SNR. FIG. 3I shows mean andstandard deviation (error bars) of the background intensity (dark gray)and the foreground intensity (light gray) for the images in FIGS. 3A-H.FIG. 3J shows variation in fluorescence distribution both across thepixels associated with one cell, and across cells. The red and bluehistograms correspond to the cell indicated by the red and blue dotsrespectively in FIG. 3H.

FIG. 4 illustrates automated localization of nanowells. FIG. 4A showsexamples of nanowells showing artifacts. FIG. 4B shows normalizedcross-correlation (NCC) for the best-fitting template. FIG. 4C showsestimated nanowell cropping regions.

FIG. 5 illustrates the pre-processing (leveling, smoothing, unmixing,illumination correction) for a sample 5×5 nanowell block. FIG. 5A showsthe presence of well outlines in the target (NALM-6 cells) channel.Panels A1 & A2 show close-up views of two selected nanowells. FIG. 5Bshows the corresponding effector (CAR⁺ T cells) channel. Panels B1 & B2correspond to the same nanowells highlighted in A1 & A2, respectively.Panels C and D show the target and effector channels afterpre-processing. The histograms on top of the images illustrate theuneven illumination in the raw image that is corrected afterpreprocessing.

FIG. 6 illustrates the ability of the confinement-constrained cellsegmentation method to recover a nanowell movie that cannot be segmentedby conventional methods. FIG. 6A shows a sample image frame for a singlenanowell containing four effector (NK) cells. The red arrow points to acell with very low contrast that is missed by conventional algorithms.The yellow arrow points to two cells that are difficult to separate dueto non-uniform fluorescence. FIG. 6B shows the proposed normalizedmulti-threshold distance map (NMTDM) improves upon the Laplacian ofGaussian (Log) response in FIG. 6C. FIG. 6D provides a histogram of cellcounts showing a variable number of cells, implying that this nanowellcannot be automatically segmented without error. Theconfinement-constrained algorithm uses the peak of this histogram(correctly at 4) to re-segment the entire movie correctly. Panels E, F,and G in FIG. 6D provide examples of re-segmentation results for under-,correct, and over segmentation scenarios.

FIG. 7 illustrates confinement-constrained cell tracking. FIG. 7A showsa sample tracking of NK cells in a nanowell. The cell outlines arecolored by cell identity. FIGS. 7B-E show color-coded sample cellmovement paths illustrating the ability to track effector and targetcells with diverse movement patterns.

FIG. 8 shows a cell interaction analysis. FIG. 8A shows spatial regionsused to compute CI for a target cell (K562 cell, green) and effector (NKcell, red). FIG. 8B shows contact event for which CI=0.3. FIG. 8C showsnon-contact for which CI=0.02. FIG. 8D shows sample frames over 10 hoursevery 30 minutes. FIG. 8E shows the contact measure CI over time fortarget cells T1 and T2. The red dotted lines correspond to the samplingtimes in FIG. 8D.

FIG. 9 shows a visual summary of segmentation and tracking resultsinvolving various target cells (NALM6, K562, and EL4) and effector cells(NK cell or CAR⁺ T cell) from 12 TIMING experiments, all run withidentical parameter settings (Table 4, Example 1). The upper rows showsample frames. The middle rows show segmentations. The bottom rows showthe cell tracks.

FIG. 10 illustrates TIMING feature analysis. FIG. 10A shows thedistribution of cumulative time in contact between effector cells andtarget cell, before and after cell death. FIG. 10B shows distributionsof displacements of NK cells before (free) or during contact with theirtarget. The bars indicate comparisons, along with their significance(*=p<0.05, **=p<0.01, ***=p<0.001, ****=p<0.0001).

FIG. 11 provides a schematic of modules for high-throughput multiplexedfunctional and molecular profiling of single cells as achieved throughcombination of beads assay (protein secretion), TIMING (cell-cellinteraction analytics) and microfluidic qPCR (single-cell geneexpression).

FIG. 12 illustrates that the frequency of IFNγ-secreting T cellsenumerated by functionalized microbeads within nanowell arrays iscorrelated to the same responses determined using ELISpot. FIG. 12Aprovides a schematic of beads assay and antibodies sandwich to detectcytokine secreted from single-cell. Effector cells and target cells werelabeled with PKH67 and PKH26 (Sigma) respectively and cytokine-positivebeads fluoresced in red (Streptavidin-PE). FIG. 12B providesbackground-corrected mean fluorescence intensity (MFI) detected from aminimum of 30 IFNγ-positive beads, as a function of IFNγ analyteconcentration. FIG. 12C provides a comparison of the bead assay withELISpot for detection of single-cell IFNγ secretion of differenteffector cells (PBMC and TIL) at varying level of antigenic stimulation(viral peptide pools and PMA/ionomycin). Linear regression show thatboth approaches are significantly correlated (r²=0.87, p-value=0.0008).

FIG. 13 provides a finite element analysis to model the efficiency ofcapture of analyte secreted from single cells in open-well systems. FIG.13A provides a snapshot of heat maps showing analyte concentration inliquid phase across the well (right) and on the bead surface (left)after 5 hours of secretion in a 40 μm nanowell. Simulation parameterswere shown on the table on the right. FIG. 13B provides a fractionaloccupancy of beads of different sizes as a function of incubation timeand their ability to capture analyte secreted from single cells. FIG.13C provides a fractional occupancy of 3 μm beads as a function ofincubation time when the binding site density was varied across threeorders of magnitude. For a single-cell secreting at a constant rate,beads with lowest binding site density possess highest fractionaloccupancy.

FIG. 14 provides a schematic of an effector cell (blue) that recognizesa CD19 antigen on a tumor cell (red) with a second generation chimericantigen receptor (CAR) that activates through CD3ζ and CD28 endodomains.

FIG. 15 provides flow cytometry characterization of the phenotype andfunction of CAR⁺ T cells. FIG. 15A provides a phenotypiccharacterization of CAR⁺ T cells with flow cytometry, showing that thecells were predominantly CD8⁺ with over 90% expression of CAR. FIG. 15Bshows dot plots obtained by staining with CD62L and CD45RA, showing thatthe dominant subset of CAR⁺ T cells (60.73%) were naïve-like. FIG. 15Cprovides intracellular staining analysis, which confirms the ability ofCAR⁺ T cells to specifically upregulate IFNγ expression upon recognitionof target cells expressing cognate antigen. The effector:target ratiowas 1:5.

FIG. 16 provides data relating to combining TIMING with bead basedassays to interrogate multi-functionality of CAR⁺ T cells at thesingle-cell level. FIG. 16A demonstrates how TIMING is utilized toquantify T-cell intrinsic behavior like motility and the nature of theirinteraction leading to induction of apoptosis within tumor cells.Effector (blue) and target (red) cells were labeled, and apoptosis wasdetected by Annexin V (green-yellow). FIG. 16B shows that, at theconclusion of the TIMING assay, the IFNγ molecules captured onto thebeads during TIMING are revealed by using appropriate fluorescentlylabeled antibodies.

FIG. 17 provides quantitative comparisons of the intrinsic andinteraction behaviors of monofunctional and polyfunctional CAR⁺ T cells.FIG. 17A provides a Venn Diagram showing breakdown of CD8⁺ T cellfunctionality based on killing (no kill, kill one, and kill multiple)and/or IFNγ secretion for nanowells containing exactly one effector andmultiple (2-5) tumor cells interactions (N=1178). FIG. 17B providescumulative contact duration between effector and targets (minutes)leading to the different functional outcomes. Effector cells that onlysecrete IFNγ (monofunctional) exhibited longer contact duration comparedto cells that kill one or serial, irrespective of whether they secreteIFNγ. Kinetics of killing based on t_(Contact) (FIG. 17C) and t_(Death)(FIG. 17D) are also shown for mono-killer and multi killer cells (first,second, and third target killed respectively) for subsets of effectorthat participate in killing and/or IFNγ secretion. FIG. 17E providesdata relating to average displacement, d_(well) (μm), calculated fordifferent combination of functionality of killing and IFNγ secretion ofCAR⁺ T cell. All p-values for all multiple comparisons were computedusing parametric one-way ANOVA and each dot represents a single effectorcell. P-value designations are as follows: *<0.05, **<0.01, ***<0.001,and ****<0.0001.

FIG. 18 provides a schematic depicting the effector parameters used todescribe their interaction with single NALM-6 tumor cells. The red barindicates periods of conjugation, the blue arrow indicates timepoint atwhich conjugation was first observed, and the green line indicates timeto target death since first conjugation.

FIG. 19 provides additional data relating to the motility of T cells.FIG. 19A provides a t_(Contact)/t_(Death) comparison for multi-killercells and mono-killer T cells. It was observed that t_(Contact) wassignificantly lower than t_(Death), demonstrating that T-cell detachmentpreceded tumor-cell Annexin V staining. P-values were determined usingparametric one-way ANOVA. FIG. 19B shows a comparison of duration ofconjugation of killers and non-killer T cells, (irrespective of IFNγsecretion). P values were determined using t-test. Note that anon-killer T cells have a population of cells, indicated by the greenarrow, that were conjugated to the tumor cell for the entire duration ofobservation and hence the values represent an underestimate of the trueduration of conjugation.

FIG. 20 shows average displacements of effector cells during conjugationof effector with target cells per frame interval (5 minutes) at an E:Tratio of 1:2-5. Effector cells that kill multiple targets irrespectiveof whether they secrete IFNγ are significantly more motile compared tonon-functional effectors that do not kill or secrete IFNγ. Each markerrepresents single-cell and red bar represents the mean of thepopulation. P-values were determined using one-way ANOVA.

FIG. 21 provides a list of targeted genes and primer designs forDELTAgene quantitative PCT (qPCR) assays in Example 2.

FIG. 22 provides data indicating that motile CD8⁺ CAR⁺ T cells displayan activated transcriptional profile. FIG. 22A provides representativeexamples of high and low motility cell tracks during the 3 hour TIMINGexperiment. X, Y coordinates are shown in microns relative to initialcell position set to the origin. Color map represents aspect ratio ofcell polarization with red denoting circular cells and increasing shadesof green and blue denoting elongated cells. M2 and M3 denote the cellsfor which corresponding supplementary movies are shown. FIG. 22B shows avolcano plot demonstrating the significance (t-test) and magnitude offold-change comparing high and low motility CD8⁺ T cells. FIG. 22C showsunsupervised hierarchical bi-clustering of samples and of the genesidentified as having a significant difference (p-value<0.05) and netfold-change of >1.5. * and + denote the individual motile and non-motilecells whose tracks are shown in panel A. FIG. 22D shows that trenddiscovery with STrenD allows selecting the genes that are the mostrelevant for description of the progressive states between cells. FIG.22E shows the visualization of the consecutive states in a tree shapestructure illustrating how each gene localizes differentially with highor low motility cells. FIG. 22F shows a protein interaction networkanalysis using Genemania of differentially expressed genes demonstratingtheir segregation into T cell activation and cell migration pathways.

FIG. 23 shows position tracks of high and low motility CD8⁺ T cellsduring 3 hours of TIMING experiments. Shown are larger scanning area andlower circularity of high motility cells as compared to low motilitycells. Cell positions were tracked by automated image analysis andgraphed using the Matlab surface function. X, Y coordinates are listedin microns relative to cell initial position. Color map representsaspect ratio (circularity) values. Red denotes circular cells andincreasing shades of green and blue indicate elongated cells.

FIG. 24 provides unsupervised hierarchical bi-clustering represented asheatmap of samples and of the genes along with the average speed andaverage aspect ratio (Min/Max) of the individual T cells. Only genes andfeatures identified as having a significant difference (p-value<0.05)and net fold-change of >1.5 are used in the clustering. Compared toclustering without speed and shape features (FIG. 22C), the samples andthe genes are clustered in a similar manner when using these features:similar accuracy in sample segregation and gene ordering.

FIG. 25 provides comparisons of relative number of Granzyme B andperforin transcripts in high and low motility CD8⁺ T cells. Transcriptnumbers were calculated as 2^((Log2Ex)) in an idealized manner, assuminga maximum efficacy of the qPCR assays. Granzyme B is not differentiallyexpressed, whereas perforin shows a significant difference between thetwo groups (p-value=0.03).

FIG. 26 shows CD19 expression on NALM-6 tumor cells as determined byimmunofluorescent staining. The EL4 cell line (CD19negative) was used asa negative control (black lines).

FIG. 27 shows a correlation between idealized numbers of transcripts andaverage speed of the cell (dwell). It is shown that CXCR3 (FIG. 27A) andCD2 (FIG. 27B) expression increase with motility of CD8⁺ T cells. FIG.27C shows that CD2 and CD58 expression are linearly correlated at thesingle-cell level. FIG. 27D shows that LAGS, CD244 (2B4), GATA3 andIL18R1 transcripts are more highly expressed in high motility incomparison to low motility cells.

FIG. 28 provides a schematic summarizing integrated T-cellfunctionality. A subset of polyfunctional CD8⁺ CAR⁺ T cells participatedin both serial killing of tumor cells and secreted IFNγ. T cellsparticipating in killing and serial killing detach faster from theirtargets and possessed a higher out of contact motility that enabled themto sample the local microenvironment. By contrast, T cells that failedto kill the attached target cell demonstrated low out of contactmotility and did not initiate detachment but were still able to secreteIFNγ. Gene expression profiling showed that high motility T cells hadhigher numbers of transcripts of perforin, and of molecules implicatedin cell activation and chemokine migration, in comparison to lowmotility T cells.

FIG. 29 provides high-throughput single-cell analysis of CAR⁺ T-cellcytolytic functionality in nanowell grids. FIG. 29A provides a schematicof second-generation CD19-specific CAR (CD19RCD28) that signals throughchimeric CD28/CD3-ζ. FIG. 29B provides representative compositemicrographs illustrating the ability of single CAR⁺ T cells to kill, andto undergo apoptosis, when incubated with tumor cells confined withinnanowells. Scale bar 50 μm. FIG. 29C provides phenotypiccharacterization of the CAR⁺ T cells from two separate donors. The totalCD3⁺CAR⁺ population was gated to reveal the frequencies of CD4⁺ and CD8⁺CAR⁺ T-cell populations. FIG. 29D provides a comparison of the cytolyticresponses measured by the single-cell assay and population-level ⁵¹Crrelease assay, at an E:T ratio of 1:1. The numbers in parentheses forthe single-cell assay report the total number of events observed.

FIG. 29E shows donut plots summarizing the frequency of killing outcomesof the interaction between CAR⁺ T cells, derived from these two donors,and CD19⁺EL4 target cells. Representative micrographs illustrating eachof these interactions are shown in FIG. 31.

FIG. 30 illustrates a high-throughput cytotoxicity assay for monitoringT-cell target cell interactions in nanowell grids. Labeled effectors andtarget cells are loaded onto a nanowell array (85,000 individual wells,125 pL each well) to enable monitoring of T-cell function at thesingle-cell level. Subsequent to loading and washing steps, the entirechip is immersed in cell-culture media containing AnnexinV. A pre-imageis acquired on the microscope to determine the occupancy of every singlenanowell and to exclude cells dead at the start of the assay. The arrayis then transferred to the incubator for 6 hours to enable cell-cellinteractions and a second post-image is acquired. In house imagesegmentation programs are used to automatically process the images anddatabase matching is employed to determine killing. In parallel, aseparate nanowell array is loaded with targets only to determine thedeath rate in the absence of effectors, over the same period of time.The killing assay results are corrected for the background killing ratedetermined by the target only arrays.

FIG. 31 provides composite micrographs illustrating representativeexamples of the interactions between single CAR⁺ T cells (E) and one ormore NALM-6 tumor (T) cells. The tumor-cells are colored red, the CAR⁺ Tcells are labeled blue with an artificial white exterior. Killing isdetermined by the colocalization of Annexin V staining (green) on redtarget cells. Scale bar 50 μm.

FIG. 32 shows donut plots summarizing the outcomes of the interactionbetween individual CAR8 cells and 1-3 CD19⁺-NALM-6 tumor cells.

FIG. 33 shows a scheme of a TIMING assay. PDMS nanowell arrays (64 pLeach nanowell) were fabricated to bond a 60 mm petridish. Labeledeffectors and targets were loaded onto the nanowell array and the entirechip was immersed in cell-culture media containing fluorescent AnnexinV. At least 6,000 nanowells are imaged every 7-10 minutes on themicroscope for a total of 12-16 hours. Subsequently, an integratedpipeline within FARSIGHT is implemented to automatically enable welldetection, image preprocessing and cell segmentation, tracking andfeature computation. The images are fragmented such that each nanowellrepresents a single time series file. When analyzing time series data,only nanowells that yielded the exact same number of effectors andtargets in >95% of time points were carried forward for analysis.Finally, the data is presented as time-series plots for each well alongwith the associated cell feature graphs.

FIG. 34 shows that CAR8 cells can be classified into different subgroupsbased on their the motility and conjugation periods with NALM-6 tumorcell (E:T 1:1). Schematic depicting the effector parameters used todescribe their interaction with single NALM-6 tumor cells are shown.FIG. 34A show a timeline, where the red bar indicates periods ofconjugation, the blue arrow indicates timepoint at which conjugation wasfirst observed, and green line indicates time to target death sincefirst conjugation. FIG. 34B shows an aspect ratio of polarization thatdescribes the ratio of major and minor axis fitted to an ellipse. FIG.34C shows an illustration of a net displacement of a T-cell centroid(d_(Well)), which represents the average displacement of the centroid ofthe effector cell between successive seven minute time points. Alsoshown are the mean motility (FIG. 34D), time to first conjugation (FIG.34E), and killing efficiency (FIG. 34F), of single CAR8 cells in each ofthree different subgroups. Each circle represents a single cell.P-values for multiple comparisons were computed using parametric one-wayANOVA.

FIG. 35 shows the identification of subgroups of killer CAR8 cells basedon their motility and contact behavior with tumor cells at an E:T ratioof 1:1. The time series of the contact pattern of CAR8 cells in theirinteraction with NALM-6 cells was clustered using K-means clustering(Euclidean distance, complete linkage) to identify low and high contactduration subsets. The displacement (d_(well)) of the CAR8 cells wasindependently clustered to yield two or three subsets using K-means(Euclidean distance, complete linkage). Since these are features of thesame cells, Caleydo was used to visualize the linkage between theclusters (gray cables) at single-cell resolution. The frequency of eachof the three subsets, S1-S3, is highlighted in orange.

FIG. 36 shows that multi-killer CAR8 cells engage in simultaneousconjugations leading to multiplexed killing (E:T 1:2-5). FIG. 36A showsa distribution of the number of simultaneous conjugations of individualCAR8 cells when incubated with increasing number of NALM-6 tumor cells.The mean motility (FIG. 36B), time to first conjugation (FIG. 36C), andkilling efficiency (FIG. 36D), of individual multi-killer CAR8 cells arealso shown. P-values for multiple comparisons were computed usingparametric one-way ANOVA.

FIG. 37 shows a subpopulation of CAR4 cells, identified based on theirmotility, can engage in efficient killing (E:T 1:1). FIG. 37A shows aphenotypic characterization of the CAR⁺ T cells from two separate donorsthat comprise of predominantly CD4⁺CAR⁺ T cells. The mean motility (FIG.37B), killing efficiency (FIG. 37C) of single CAR4 cells in each ofthree different subgroups are also shown. FIG. 37D provides a comparisonof the means of the killing efficiencies between single CAR8 and CAR4cells within the S1 subgroups. Each circle represents a single cell inpanels B-D. CAR4 cells are represented using grey circles and CAR8 cellsare represented using black circles. FIG. 37E provides a comparativeKaplan-Meier estimators depicting the differences in killingefficiencies of the entire population of CAR4 cells and CAR8 cells.P-values for multiple comparisons (B/C) were computed using a parametricone-way ANOVA, and dual comparisons (D/E) computed using unpairedtwo-tailed t-test.

FIG. 38 provides the identification of subgroups of killer CAR4 cellsbased on their motility and contact behavior with tumor cells at an E:Tratio of 1:1. The time series of the contact pattern of CAR4 cells intheir interaction with NALM-6 cells was clustered using K-meansclustering (Euclidean distance, complete linkage) to identify low andhigh contact duration subsets. The displacement (d_(well)) the CAR4cells was independently clustered to yield two or three subsets usingK-means (Euclidean distance, complete linkage). Since these are featuresof the same cells, Caleydo was used to visualize the linkage between theclusters (gray cables) at single-cell resolution. The frequency of eachof the three subsets, S1-S3, is highlighted in orange.

FIG. 39 shows that multi-killer CAR4 cells demonstrated delayed kineticsof killing in comparison to CAR8 cells (E:T 1:2-5). Comparisons betweenthe mean motility (FIG. 39A), and killing efficiency (FIG. 39B) are alsoshown. FIG. 39C provides a box and whisker plots (extremities indicate99% confidence intervals) displaying intracellular expression ofGranzyme B identified by immunofluorescent staining and flow-cytometry.CAR4 cells (from donors PB5858 and PB333038) and CAR8 cells (from donorsPB243566 and PB281848) were profiled using mAb against CD4/CD8/CAR andGzB. P-values were computed using parametric one-way ANOVA for multiplecomparisons or t-tests for dual comparisons. FIG. 39D shows flowcytometric killing assay (E:T=5:1) of CAR4 cells incubated with threeseparate target cell lines (Daudi-β2m, NALM-6 and CD19⁺EL4) in theabsence or presence of 5 mM EGTA blockade.

FIG. 40 shows that the frequency and kinetics of killer-cell apoptosisare dependent on functional conjugations with multiple NALM-6 tumorcells. FIG. 40A provides comparisons of the mean kinetics of effectorapoptosis of individual single killer CAR⁺ T cells (E:T 1:1) withmulti-killer CAR⁺ T cells (E:T 1:2-5). Each circle represents asingle-cell. CAR4 cells are represented using grey circles and CAR8cells are represented using black circles. FIG. 40B shows the frequencyof killer-cell apoptosis as a function of tumor cell density.

FIG. 41 provides a scheme of a TIMING assay followed by single-cell geneexpression profiling.

FIG. 42 provides motility data for CAR⁺ T cell constructs in vitro.

FIG. 43 provides cytotoxicity data for CAR⁺ T cell constructs in vitro.

FIG. 44 provides a gene expression profile of CAR⁺ T cells.

FIG. 45 provides a Venn diagram of differentially expressed genes fromthree donors.

FIG. 46 provides data indicating that CD137 is expressed at higherlevels in activated CAR⁺ T cells and in degranulating CAR⁺ T cells.

FIG. 47 provides data indicating that CD137 stimulation decreasesexhaustion markers while TIM3 targeting induces CTLA4.

FIG. 48 provide data indicating that targeting CD137 and TIM3 increasescytotoxicity of CAR⁺ T cells.

FIG. 49 provides data indicating that the targeting of CD137 and TIM3increases CAR⁺ T cell cytotoxicity.

DETAILED DESCRIPTION

It is to be understood that both the foregoing general description andthe following detailed description are illustrative and explanatory, andare not restrictive of the subject matter, as claimed. In thisapplication, the use of the singular includes the plural, the word “a”or “an” means “at least one”, and the use of “or” means “and/or”, unlessspecifically stated otherwise. Furthermore, the use of the term“including”, as well as other forms, such as “includes” and “included”,is not limiting. Also, terms such as “element” or “component” encompassboth elements or components comprising one unit and elements orcomponents that comprise more than one unit unless specifically statedotherwise.

The section headings used herein are for organizational purposes and arenot to be construed as limiting the subject matter described. Alldocuments, or portions of documents, cited in this application,including, but not limited to, patents, patent applications, articles,books, and treatises, are hereby expressly incorporated herein byreference in their entirety for any purpose. In the event that one ormore of the incorporated literature and similar materials defines a termin a manner that contradicts the definition of that term in thisapplication, this application controls.

Integrative quantification of single-cell dynamic functional behaviorand the underlying mechanisms responsible for the functions is importantfor developing a comprehensive understanding of cellular behaviors. Forinstance, quantifying the heterogeneity at the single-cell level inhigh-throughput across multiple biological dimensions from the genomeand transcriptome, to intracellular and extracellular signaling, and tointeraction with other kinds of cells can have a direct impact inimproving therapeutic discovery in biotechnology, diagnosis of diseases,and in facilitating immunotherapy.

While flow cytometry is an optimal tool for providing snapshots of thecellular phenotype, it is not well suited for studying continuousdynamic cellular behaviors. To characterize the complete identity ofindividual single cells, it is desirable to have a modular method thatcan quantify and screen for cellular functionality such as motility,interaction with other cells, and protein secretion; and the ability tointegrate these parameters with single-cell multiplexed molecularplatforms.

The study of such cell behaviors are of vital interest in many fields,including immunology, cancer biology, and stem cell engineering. Forinstance, T cells are an essential component of the adaptive immuneresponse against pathogens and tumors. A critical hallmark of a robustadaptive immune response against pathogens and tumors is the ability ofindividual T cells to participate in multiple functions(polyfunctionality).

T cells play an important role in mediating anti-tumor immunity.Moreover, the presence of tumor infiltrating lymphocytes (TILs) is apositive clinical prognostic marker for certain tumors. Among the mostwell described functional attributes of T-cell anti-tumor efficacy aremotility (tumor-trafficking and infiltration), direct cytotoxicity(release of cytotoxic molecules) and secretion of the pro-inflammatorycytokines like IFN-γ.

Unlike cytotoxicity that only influences the target cell that isdirectly conjugated to the T cell, secretion of IFN-γ has a moreprofound influence on all cells within the microenvironment by multiplemechanisms including elevated expression of HLA-class I molecules,induction of chemokines that promote immune cell infiltration, mediationof angiostasis, and prevention of the outgrowth of antigen-lossvariants. In addition, secretion of IFN-γ can induce adaptive resistancemechanisms in tumors by inducing the expression of T-cell suppressivemolecules and down-modulation of tumor antigen expression.

Direct measurement of all of the aforementioned T cell functions at thesingle-cell level requires the simultaneous monitoring of multipleparameters, including cell-cell interactions, cell migration, geneexpression, the ability to detect secreted proteins, and the survival ofthe effector cells. These challenges have been tackled by measuring justa subset of these effector functions and relying on correlative studiesto establish a link to cellular functionality.

Indeed, while multi-photon microscopy is useful for studying T-cellmotility and cytotoxicity in situ or in vivo, the number of T cells thatcan be simultaneously tracked is small and limited to the field-of-view,potentially leading to sampling bias. In vitro dynamic imaging systemsmay be better suited for studying the longitudinal interactions betweenT cells and target cells at single-cell resolution, in a definedenvironment and high-throughput.

Likewise, microfabricated nanowell arrays are ideal for tracking boththe motility and interaction between cells. While elegant methods likemicroengraving and the single-cell barcode chip (SCBC) have beenreported for the analysis of cytokines secreted by single cells, thesemethods require capture of the secreted cytokine on a separate glasssubstrate via encapsulation. Significantly, there are as yet no reportsdocumenting the simultaneous measurement of motility, T-cell target-cellinteraction parameters including the kinetics of killing, and cytokinesecretion quantified within the same timeframe.

Automated time-lapse microscopy of live cells in vitro is awell-established method for spatiotemporal recording of cells andbiomolecules, and tracking multi-cellular interactions. Unfortunately,most conventional methods assess limited numbers (e.g., 10-100) ofmanually sampled “representative” cell pairs, leading to subjectivebias. Therefore, such methods lack the ability to quantify the behaviorsof statistically under-represented cells reliably. The aforementionedlimitation is significant because many biologically relevant cellularsubpopulations (e.g., tumor stem cells, multi-killer immune cells, andbiotechnologically relevant protein secreting cells) are rare.

As such, there is a need for improved and real-time methods of studyingcellular activity that integrate dynamic cellular behavior withmolecular behavior at the single-cell level. The present disclosureaddresses the aforementioned need.

In some embodiments, the present disclosure pertains to methods ofevaluating cellular activity. In some embodiments illustrated in FIG. 1,the methods of the present disclosure include one or more of thefollowing steps: obtaining a cell population (step 10); placing the cellpopulation on an area (step 12); assaying for a dynamic behavior of thecell population as a function of time (step 14); identifying one or morecells of interest based on the dynamic behavior (step 16);characterizing a molecular profile of the one or more identified cells(step 18); correlating the obtained information (step 20); and utilizingthe correlated information (step 22).

In some embodiments, the methods of the present disclosure may utilize asensor. In additional embodiments, the present disclosure pertains tomethods of evaluating cellular activity by placing a cell population onan area that is associated with a sensor, and assaying for a dynamicbehavior of the cell population as a function of time. As set forth inmore detail herein, the methods of the present disclosure can havenumerous embodiments.

Obtaining Cell Populations

The methods of the present disclosure can obtain cell populations fromvarious sources. For instance, in some embodiments, cell populations areobtained from a tissue. In some embodiments, cell populations areobtained from a blood sample. In some embodiments, cell populations areobtained from an in vitro expanded blood cell population. In someembodiments, cell populations are obtained directly from a patient'sblood before or after a treatment.

The methods of the present disclosure can also utilize various methodsto obtain cell populations. For instance, in some embodiments, cellpopulations are obtained by a method that includes, without limitation,flow cytometry, positive flow sorting, negative flow sorting, magneticsorting, and combinations thereof. In some embodiments, cell populationsare obtained by using a micromanipulator (e.g., an automated or manualmicromanipulator). In some embodiments, cell populations are obtained byusing a magnetic head after the incubation of cells with magneticparticles specific for a particular cell population phenotype.

Cell Populations

The methods of the present disclosure can obtain and utilize variouscell populations. For instance, in some embodiments, the cellpopulations include, without limitation, plant cells, fungi cells,bacterial cells, prokaryotic cells, eukaryotic cells, unicellular cells,multi-cellular cells, immune cells, and combinations thereof. In someembodiments, the cell populations include immune cells. In someembodiments, the immune cells are obtained from a patient's blood beforeor after a treatment. In some embodiments, immune cells are expanded invitro.

In some embodiments, the cell populations include, without limitation, Tcells, B cells, monocytes, macrophages, neutrophils, dendritic cells,natural killer cells, fibroblasts, stromal cells, stem cells, progenitorcells, tumor cells, tumor stem cells, tumor infiltrating lymphocytes,and combinations thereof. In some embodiments, the cell populationincludes T cells. In some embodiments, the T cells include, withoutlimitation, helper T cells, cytotoxic T cells, natural killer T cells,genetically modified T cells, chimeric antigen receptor (CAR) modified Tcells, and combinations thereof. In some embodiments, the T cellsinclude, without limitation, CD3⁺ T cells, γδ T cells (Vγ9⁺,Vγ2⁺),natural killer T cells (CD1d⁺, Vα24⁺), and combinations thereof.

In some embodiments, the cell populations include natural killer cells.In some embodiments, the natural killer cells include, withoutlimitation, CD16⁺ natural killer cells, natural killer T cells,CD1d⁺/Vα24⁺ natural killer T cells, and combinations thereof.

In some embodiments, the cell populations include tumor cells. Tumorcells may be derived from various sources. For instance, in someembodiments, the tumor cells are derived from at least one of cancerstem cells, melanoma, pancreatic cancer, ovarian cancer, leukemia,lymphoma, breast cancer, glioblastoma, neuroblastoma, prostate cancer,lung cancer, and combinations thereof. In some embodiments, the tumorcells include NALM cells.

The cell populations of the present disclosure can be homogenous orheterogenous. For instance, in some embodiments, the cell populationsare homogenous. In some embodiments, the cell populations areheterogenous. In some embodiments, the heterogenous cell populationsinclude tumor cells and immune cells. In some embodiments, theheterogenous cell populations include tumor cells and cytotoxic T cells.

Placement of Cell Populations on an Area

The cell populations of the present disclosure can be placed on variousareas for dynamic behavior analysis. For instance, in some embodiments,the area is non-encapsulated. In some embodiments, the area is an opensystem.

In some embodiments, the area includes a volume bounded container. Insome embodiments, the area includes a plurality of containers. In someembodiments, the containers are in the form of at least one of wells,channels, compartments, and combinations thereof. In some embodiments,the containers include nanowells. In some embodiments, the containersare in the form of an array.

In some embodiments, are area may include containers that have a volumecapacity of about 1 nL to about 100 nL. In some embodiments, thecontainers have a volume capacity of less than about 1 nL. In someembodiments, the area is in the form of a patterned array of micro ornanowells. In some embodiments, the area is associated with fluid flowto permit gas and nutrient exchange.

In some embodiments, the area includes a number of individual arrays ona microfluidic chip with a plurality of individual containers (e.g.,from about 10 containers to about 1,000,000 containers). In someembodiments, the areas of the present disclosure include microfluidicchips that contain arrays of nanowells with volume capacities of lessthan about 1 nL per well.

The areas of the present disclosure may be fabricated from variousmaterials. For instance, in some embodiments, the areas of the presentdisclosure include, without limitation, polydimethylsiloxane (PMDS),polymethylmethacrylate (PMMA), silicon, glass, polyethylene glycol(PEG), and combinations thereof.

Cell populations can be placed on the areas of the present disclosure invarious manners. For instance, in some embodiments, cell populations areplaced on an area as individual cells. In some embodiments, cellpopulations are placed on an area as an aggregate of cells. In someembodiments, cell populations are placed on an area as a small number ofcells (e.g., 2-6 cells per container). In some embodiments, cellpopulations are placed on the area in the form of droplets.

In some embodiments, cell populations are placed on an area manually. Insome embodiments, cell populations are placed on an area in an automatedmanner. In some embodiments, cell populations are placed on an area bysemi-automated cell retrieval methods. In some embodiments, cellpopulations are placed on an area by sorting specific droplets of cells.

Dynamic Behavior

The methods of the present disclosure may be utilized to assay variousdynamic behaviors of cell populations on an area. For instance, in someembodiments, the assayed dynamic behavior includes, without limitation,cellular activation, cellular inhibition, cellular interaction, proteinexpression, protein secretion, metabolite secretion, changes in lipidprofiles, microvesicle secretion, exosome secretion, microparticlesecretion, changes in cellular mass, cellular proliferation, changes incellular morphology, motility, cell death, cell cytotoxicity, celllysis, cell membrane polarization, establishment of a synapse, dynamictrafficking of proteins, granule polarization, calcium activation,metabolic changes, and combinations thereof.

In some embodiments, the assayed dynamic behavior includes proteinsecretion. In some embodiments, the assayed dynamic behavior includesmotility. In some embodiments, the assayed dynamic behavior includescell death, such as activation induced cell death.

In some embodiments, the assayed dynamic behavior includes cellularinteraction. In some embodiments, the cellular interaction includes,without limitation, heterologous cellular interaction, homologouscellular interaction, and combinations thereof.

In some embodiments, the assayed dynamic behavior includes thecombination of cell death and cellular interaction. In some embodiments,the assayed dynamic behavior includes, without limitation, motility,cell cytotoxicity, cell death, protein secretion, cellular interaction,and combinations thereof. For instance, in some embodiments, the dynamicbehavior to be assayed includes secretion of cytokines from a T-cell(e.g., pro-inflammatory cytokines, such as IFN-γ), the motility of theT-cell, and the interaction of the T-cell with a target cell, dynamicmonitoring of T-cell/target cell death, and combinations thereof.

In some embodiments, the assayed dynamic behavior includes a change incellular morphology. In some embodiments, the change in cellularmorphology includes, without limitation a change in cell shape, a changein cell volume, a change in cell mass, a change in cell size, a changein cell polarization, and combinations thereof.

Assaying of Dynamic Behaviors

Various methods may be utilized to assay the dynamic behavior of cells.In some embodiments, the assaying occurs at a single cell level. In someembodiments, the assaying occurs by visualizing the dynamic behavior. Insome embodiments, the visualizing occurs by a method that includes,without limitation, microscopy, time-lapse imaging microscopy,fluorescence microscopy, multi-photon microscopy, quantitative phasemicroscopy, surface enhanced Raman spectroscopy, videography, manualvisual analysis, automated visual analysis, and combinations thereof.

In some embodiments, the visualizing of dynamic behavior occurs bytime-lapse imaging microscopy. In some embodiments, the visualizing isrecorded as an array of multi-channel movies. In some embodiments, thevisualizing occurs through high-throughput time-lapse imaging microscopyin nanowell grids. In some embodiments, the visualizing occurs byutilizing time-lapse microscopy through at least one of bright fieldmicroscopy, phase contrast microscopy, fluorescence microscopy,quantitative phase microscopy, surface enhanced Raman spectroscopy, andcombinations thereof.

In some embodiments, the assaying of a dynamic behavior includesquantification of the dynamic behavior. In some embodiments, theassaying occurs manually. In some embodiments, the assaying occursautomatically. In some embodiments, the assaying occurs automaticallythrough the use of algorithms. For instance, in some embodiments, theassaying occurs through the use of automated quantification of a dynamicbehavior through automated algorithms that measure the onset time,duration, frequency, and extent of the dynamic behavior.

The assaying of the dynamic behavior of cells can have variousembodiments. For instance, in some embodiments, the cellular morphologyof a cell population is assayed by measuring the eccentricity of abest-fitting ellipse.

In some embodiments, the motility of a cell population is assayed byevaluating at least one of cellular location, cellular movement,cellular displacement, cellular speed, cellular movement paths on anarea, cellular infiltration, cellular trafficking, and combinationsthereof. In some embodiments, cell positions can be tracked by automatedimage analysis and graphed using a Matlab surface function.

In some embodiments, cell death is assayed by detecting apoptosismarkers. In some embodiments, cellular toxicity is assayed by measuringrelease of cytotoxic molecules from the cell population.

In some embodiments, cellular interaction of a cell population isassayed by measuring duration of cellular interactions, number ofcellular interactions, calcium activation, granule polarization, proteinlocalization, motility during cellular interaction, termination ofcellular interaction, and combinations thereof. In some embodiments, theassaying of the cellular interaction also includes the detection andquantification of cell-cell contacts.

In some embodiments, the combination of cell death and cellularinteraction are assayed by evaluating various parameters. Suchparameters can include, without limitation, time between first cellularcontact and death, the number of cellular contacts prior to cell death,cumulative duration of cellular interactions between first cellularcontact and target cell death (t_(Contact)), time between first cellularcontact and target cell death (t_(Death)), time between termination ofcellular contact and target cell death, number of cell deaths caused byan individual cell, and combinations thereof.

The assaying methods of the present disclosure can also includeadditional steps. For instance, in some embodiments, the assayingincludes labeling the cell population. In some embodiments, the cellpopulation is labeled by staining cells with fluorescent-based detectionreagents. In some embodiments, the labeling can provide information onvarious dynamic behaviors, such as cell death, motility, or proteinsecretion. For instance, in some embodiments, intracellular staininganalysis can be utilized to assay protein expression (e.g.,up-regulation of IFNγ expression using fluorescent immune-affinityreagents, such as antibodies). In some embodiments, the labeling ofcells with fluorescent dyes can be utilized to indicate the viability ofthe cells.

In some embodiments, the assaying includes pre-treating the cellpopulation with an active agent. In some embodiments, the active agentincludes, without limitation, small molecules, drugs, antibodies,cytokines, chemokines, growth factors, and combinations thereof.

In some embodiments, the assaying includes pre-treating the cellpopulation with other cells. In some embodiments, the other cells caninclude cells of the same species, pathogens or symbiotes. In someembodiments, the other cells can include, without limitation, viruses,bacteria, parasites, and combinations thereof.

The assaying methods of the present disclosure can occur under variousconditions. For instance, in some embodiments, the step of assaying thedynamic behavior of cells is performed at 37° C. and 5% CO₂. In someembodiments, the step of assaying the dynamic behavior of cells isperformed at varying concentrations of molecular oxygen (e.g., 0-5%). Insome embodiments, the step of assaying the dynamic behavior of cells isperformed at varying concentrations of metabolites. In some embodiments,the metabolites include, without limitation, glucose, glutamine,lactate, branched chain amino acids and pyruvate. Additional conditionscan also be envisioned.

Assaying of Dynamic Behavior as a Function of Time

The dynamic behavior of cells can be assayed for various periods oftime. For instance, in some embodiments, the assaying occurs atsequential intervals for a period of time. In some embodiments, theperiod of time ranges from about 1 minute to about 96 hours. In someembodiments, the period of time ranges from about 1 minute to about 24hours. In some embodiments, the period of time ranges from about 1 hourto about 24 hours. In some embodiments, the period of time ranges fromabout 5 hours to about 24 hours. In some embodiments, the period of timeranges from about 12 hours to about 14 hours.

In some embodiments, the sequential intervals range from about 1 minuteto about 60 minutes. In some embodiments, the sequential intervals rangefrom about 1 minute to about 10 minutes. In some embodiments, thesequential intervals range from about 5 minutes to about 10 minutes. Insome embodiments, the sequential intervals range from about 5 minutes toabout 6 minutes.

In some embodiments, the dynamic behavior of cells are assayed for 12-13hour periods at sequential intervals that last from about 5 minutes toabout 10 minutes per interval. In some embodiments, the dynamic behaviorof cells are assayed for about 8 hours at sequential intervals that lastfor about 6 minutes per interval.

Sensors

In some embodiments, the assaying of the dynamic behavior of a cellpopulation occurs by the use of sensors. In some embodiments, the sensorto be used for assaying a dynamic behavior is associated with the areathat contains the cell population. In some embodiments, the sensor isimmobilized on the area.

The sensors of the present disclosure can include various components.For instance, in some embodiments, the sensor includes an analytebinding agent. In some embodiments, the analyte binding agent isassociated with one or more regions on a surface of the sensor. In someembodiments, the analyte binding agent includes, without limitation,genes, nucleotide sequences, interference RNA (RNAi), antisenseoligonucleotides, peptides, antisense peptides, antigene peptide nucleicacids (PNA), proteins, antibodies, and combinations thereof.

In some embodiments, the analyte binding agent on a sensor is directedagainst an analyte of interest (i.e., an analyte associated with adynamic behavior of a cell). In some embodiments, the analyte ofinterest includes, without limitation, secreted proteins, cell lysatecomponents, cellular receptors, metabolites, lipids, microvesicles,exosomes (e.g., exosomes with diameters of less than about 200 nm),microparticles (e.g., microparticles with diameters between about 200 nmand about 5 μm), small molecules, protons, carbohydrates, andcombinations thereof. In some embodiments, the analyte of interest is acytokine.

In some embodiments, the analyte of interest is captured by the sensorsof the present disclosure. In some embodiments, the captured analytes ofinterest are subsequently characterized. The captured analytes ofinterest may be characterized by various methods. In some embodiments,such methods can include, without limitation, mass spectrometry,sequencing, microscopy, nucleic acid hybridization, immunoassay-baseddetection (e.g., enzyme-linked immunosorbent assay (ELISA)), andcombinations thereof.

The sensors of the present disclosure can be in various forms. Forinstance, in some embodiments, the sensor is in the form of a bead. Insome embodiments, the bead is coated with an antibody directed againstan analyte (e.g., antibody-coated beads to profile cytokine secretion,as detected with fluorescently labeled secondary antibodies).

The beads of the present disclosure can have various diameters. Forinstance, in some embodiments, the beads include diameters that rangefrom about 100 nm to about 100 μm. In some embodiments, the beadsinclude diameters that range from about 500 nm to about 10 μm. In someembodiments, the beads include diameters that range from about 1 μm toabout 10 μm. In some embodiments, the beads include diameters that rangefrom about 500 nm to about 5 μm. In some embodiments, the beads includediameters ranging from about 1 μM to about 6 μM. In some embodiments,the beads include diameters that range from about 3 μm to about 5 μm. Insome embodiments, the beads include diameters that range from about 1 μmto about 3 μm. In some embodiments, the beads include diameters of about3 μm.

The beads of the present disclosure can include various analyte bindingdensities. For instance, in some embodiments, the beads include abinding site density ranging from about 10⁻¹⁰ mol/m² to about 10 mol/m².In some embodiments, the beads include a binding site density rangingfrom about 10⁻⁹ mol/m² to about 10⁻¹ mol/m². Additional binding sitedensities can also be envisioned.

The beads of the present disclosure can also include variouscompositions. For instance, in some embodiments, the beads can includepolymeric beads, silicon beads, glass beads, and combinations thereof.

The beads of the present disclosure can be modified with an analytebinding agent through various methods. For instance, in someembodiments, the beads may be co-incubated with an antibody against ananalyte of interest. This can then result in the adhesion of theantibodies to the surfaces of the beads. The beads can then be used toassay a dynamic behavior of a cell population.

Use of Sensors to Assay Dynamic Behavior

The sensors of the present disclosure can be utilized to assay thedynamic behavior of cells in various manners. For instance, in someembodiments, the sensors of the present disclosure are utilized to assaythe dynamic behavior of a cell population in real-time. In someembodiments, the sensors of the present disclosure are utilized to assaythe dynamic behavior of a single cell in a cell population in real-time.

The sensors of the present disclosure can be utilized to assay variousdynamic behaviors of a cell population. Such dynamic behaviors andassaying methods were described previously. For instance, in someembodiments, the dynamic behavior to be assayed by the sensors of thepresent disclosure can include, without limitation, cellular activation,cellular inhibition, protein secretion, microvesicle secretion, exosomesecretion, microparticle secretion, metabolite secretion, small moleculesecretion, proton secretion, protein expression, and combinationsthereof.

In some embodiments, protein expression is assayed by the sensors of thepresent disclosure through capture of cell lysate components. In someembodiments, protein secretion is assayed by the sensors of the presentdisclosure through capture of secreted proteins. The capture of the celllysate components or secreted proteins can then be visualized by variousmethods, such as the use of fluorescent secondary antibodies.

The sensors of the present disclosure can also have secondary uses inassaying dynamic behavior. For instance, in some embodiments, the sensoris utilized as a fiduciary marker to enable auto-focusing of a cellpopulation during imaging. In some embodiments that utilize quantitativephase imaging, the invariant size of the sensor bead is used as areference object.

The cell populations of the present disclosure can be exposed to thesensors of the present disclosure by various methods. For instance, insome embodiments, the cell population is incubated with the sensors. Insome embodiments, the cell population is lysed prior to incubation withthe sensors.

Identifying One or More Cells of Interest

The assayed dynamic behavior of a cell population can be utilized toidentify one or more cells of interest. For instance, in someembodiments, one or more cells of interest can be identified based ontheir assayed motility, cell cytotoxicity, cell death, proteinsecretion, cellular interaction, and combinations thereof. In someembodiments, a single cell is identified based on the assayed dynamicbehavior. In some embodiments, a plurality of cells are identified basedon the assayed dynamic behavior.

Cell identification can occur by various methods. For instance, in someembodiments, the one or more cells are identified manually. In someembodiments, the one or more cells are identified automatically. In someembodiments, the one or more cells are identified automatically throughthe use of algorithms. In some embodiments, the one or more cells areidentified through the use of automated segmentation and trackingalgorithms.

In some embodiments, the one or more identified cells may be isolated.In additional embodiments, the methods of the present disclosure mayinclude a step of isolating the one or more identified cells. Variousmethods may be used to isolate the one or more identified cells. Forinstance, in some embodiments, the one or more identified cells areisolated by micromanipulation (e.g., manual or automatedmicromanipulation), magnetic retrieval, dielectrophoretic retrieval,acoustic retrieval, laser based retrieval, and combinations of suchsteps.

Molecular Profile Analysis

Once one or more cells are identified based on an assayed dynamicbehavior (and optionally isolated), their molecular profile can becharacterized. Various molecular profiles of the one or more identifiedcells can be characterized. For instance, in some embodiments, themolecular profile can include, without limitation, transcriptionactivity, transcriptomic profile, gene expression activity, genomicprofile, protein expression activity, proteomic profile, proteininteraction activity, cellular receptor expression activity, lipidprofile, lipid activity, carbohydrate profile, microvesicle activity,glucose activity, metabolic profile (e.g., by using mass spectrometry orother methods), and combinations thereof.

In some embodiments, the characterized molecular profile includescellular receptor expression activity. In some embodiments, the profiledcellular receptor includes, without limitation, T cell receptors,immunoglobulin receptors, killer immunoglobulin receptors (KIR), B cellreceptors (BCR), chemokine receptors (e.g., CXCR3), transcription factorreceptors (e.g., GATA3), and combinations thereof. In some embodiments,the characterized molecular profile includes one or more apoptosismarkers of a cell.

Various methods may be utilized to characterize the molecular profile ofcells. For instance, in some embodiments, the molecular profilecharacterization occurs by DNA analysis, RNA analysis, protein analysis,lipid analysis, metabolite analysis (e.g., glucose analysis), massspectrometry, and combinations thereof.

In some embodiments, the molecular profile characterization occurs byDNA analysis. In some embodiments, the DNA analysis includesamplification of DNA sequences from one or more identified cells. Insome embodiments, the amplification occurs by the polymerase chainreaction (PCR).

In some embodiments, the molecular profile characterization occurs byRNA analysis. In some embodiments, the RNA analysis includes RNAquantification. In some embodiments, the RNA quantification occurs byreverse transcription quantitative PCR (RT-qPCR), multiplexed qRT-PCR,fluorescence in situ hybridization (FISH), and combinations thereof.

In some embodiments, the molecular profile characterization occurs byRNA or DNA sequencing. In some embodiments, the RNA or DNA sequencingoccurs by methods that include, without limitation, whole transcriptomeanalysis, whole genome analysis, barcoded sequencing of whole ortargeted regions of the genome, and combinations thereof. In someembodiments, the microvesicles, exosomes or microparticles secreted bythe individual cells or aggregates are detected by RNA-sequencing orantibody-based methods.

In some embodiments, the molecular profile characterization occurs byprotein analysis. In some embodiments, the protein analysis occurs atthe proteomic level. In some embodiments, the protein analysis occurs bymultiplexed fluorescent staining. In some embodiments, the comprehensivemetabolic profile of single cells is achieved by using massspectrometry.

Correlation of the Obtained Information

Various procedures may be utilized to correlate the information obtainedthrough the methods of the present disclosure. For instance, in someembodiments, the correlating includes integrating the assayed dynamicbehavior and the characterized molecular profile of the one or moreidentified cells.

In some embodiments, the correlating includes correlating the motilityof the one or more identified cells to gene expression or transcriptionactivities of the one or more identified cells. In some embodiments,gene analyses algorithms (e.g. Trend discovery with STrenD) can beutilized to select genes that are correlated with high or low motilitycells. Likewise, in some embodiments, bi-clustering algorithms may beutilized to identify over-expressed genes that are associated with highor low motility cells.

In some embodiments, the correlating includes correlating the cellularinteraction activity of one or more identified cells to the proteinexpression activity of the one or more identified cells. In someembodiments, the correlating includes correlating the motility of theone or more identified cells to the protein interaction activity of theone or more identified cells. For instance, in some embodiments, aprotein interaction network analysis of one or more identified cells canbe performed by using a Genemania algorithm that correlates the proteininteraction activity of one or more identified cells to the motility ofthe one or more identified cells. In some embodiments, the correlatinginvolves linking the ability of immune cells to participate in killingor serial killing with the genes associated with these cells usingsingle-cell RNA-seq or qPCR profiling.

Application of Obtained Information

The correlated information obtained from the methods of the presentdisclosure can be utilized for various purposes. For instance, in someembodiments, the correlated information can be utilized for at least oneof predicting clinical outcome of a treatment, screening cells,retrieving cells for further evaluation, facilitating a treatment,diagnosing a disease, monitoring cellular activity, and combinationsthereof.

In some embodiments, the correlated information can be utilized tofacilitate a treatment. In some embodiments, the treatment includesimmunotherapy. For instance, in some embodiments, the ability todynamically profile interactions between immune cells and tumor cellsand performing subsequent proteomic/transcriptomic profiling on theimmune cells allows for engineering of better immunotherapies.

In some embodiments, the correlated information can be utilized tomonitor cellular activity. In some embodiments, the monitored cellularactivity includes an immune response.

In some embodiments, the correlated information can be utilized toscreen cells, such as the screening of cells for clinical efficacy. Forinstance, in some embodiments, the screened cells include multi-killer Tcells. In some embodiments, the functional and molecular characteristicsof the multi-killer T-cells are evaluated before selecting subsets forpreclinical and clinical tests.

In some embodiments, the correlated information can be utilized topredict clinical outcome, such as the outcome of an immunotherapy. Forinstance, in some embodiments, the observed ability of a T cell topersist and participate in serial killing of tumor cells can be utilizedas a predictor of the therapeutic success of the identified T-cell incancer therapy. Likewise, the characterized protein expression activityof the identified T-cell can be utilized to introduce various markers(e.g., immune-receptors) onto the T-cell in order to enhance therapeuticsuccess in vivo.

In some embodiments, the correlated information can be utilized toretrieve cells for further evaluation. In some embodiments, cells areretrieved by various methods, such as micromanipulation. Thereafter, thecells are evaluated for various purposes. In some embodiments, the cellsare evaluated in additional studies. In some embodiments, the cells areevaluated through cellular expansion.

Additional Embodiments

Reference will now be made to more specific embodiments of the presentdisclosure and experimental results that provide support for suchembodiments. However, Applicants note that the disclosure below is forillustrative purposes only and is not intended to limit the scope of theclaimed subject matter in any way.

Example 1. Automated Profiling of Individual Cell-Cell Interactions fromHigh-Throughput Time-Lapse Imaging Microscopy in Nanowell Grids (TIMING)

In this Example, fluorescently labeled human T cells, Natural Killercells (NK), and various target cells (NALM6, K562, EL4) wereco-incubated on PDMS arrays of sub-nanoliter wells (nanowells), andimaged using multi-channel time-lapse microscopy. Novel cellsegmentation and tracking algorithms that account for cell variabilityand the nanowell confinement property increased the yield of correctlyanalyzed nanowells from 45% (existing algorithms) to 98% for wellscontaining one effector and a single target. This enabled reliableautomated quantification of cell locations, morphologies, movements,interactions, and deaths. Automated analysis of recordings from 12different experiments demonstrated automated nanowell delineationaccuracy of more than 99%, automated cell segmentation accuracy of morethan 95%, and automated cell tracking accuracy of 90%, with defaultparameters, despite variations in illumination, staining, imaging noise,cell morphology, and cell clustering. Analysis of a dataset with morethan 10,000 nanowells revealed that NK cells efficiently discriminatebetween live and dead targets by altering the duration of conjugation.The data also demonstrated that cytotoxic cells display higher motilitythan non-killers, both before and during contact.

Recent advances have enabled the fabrication of large arrays ofsub-nanoliter wells (nanowells) cast onto transparent biocompatiblepolydimethylsiloxane (PDMS) substrates. Small groups of living cellsfrom clinical samples, and laboratory-engineered cells can be confinedto nanowells, and imaged over extended durations by multi-channeltime-lapse microscopy, allowing thousands of controlled cellular eventsto be recorded as an array of multi-channel movies. Applicants refer tothis method as Time-lapse Imaging Microscopy In Nanowell Grids (TIMING).The spatial confinement can enable a rich sampling of localized cellularphenomena, including cell movements, cellular alterations, and cell-cellinteraction patterns, along with the relevant intra-cellular eventmarkers.

TIMING is thus ideally suited for tracking cell migration andinteractions at short distances. However, if cell migratory patternsover larger distances are of interest, arrays with larger wells can befabricated. Similarly, if unconfined migratory behavior of cells isdesired, other methods have been described. The promise and challenge ofnanowell arrays, is high throughput, eliminating the need for userselection of events of interest, and the ability to repeatedly followthe same cell(s) over time.

For instance, FIG. 2 illustrates a TIMING dataset consisting of 11,760nanowells containing fluorescently tagged human CAR⁺ T-cells (red) andNALM-6 tumor cells (green) that were imaged by time-lapse microscopyover 130 time points at 5-minute intervals to yield an array of4-channel movies, one per nanowell. The border nanowells from each blockare discarded, yielding 25 usable nanowells per block. TIMING datasetsvary in size between 200 GB-1.5 TB, depending upon the array size, andnumber of time points. Production datasets are often of lower qualitythan the example in FIG. 2 (e.g., FIGS. 3-5) and contain confoundsincluding natural cellular variability, variations in signal to noiseratio (SNR), staining variations, focus drift, spectral overlap betweenfluorochromes, and photobleaching.

In this Example, Applicants demonstrate the development of highlyautomated algorithms that can reliably segment and track the cells inTIMING datasets with minimal parameter tuning, and yield a sufficientlylarge and rich set of cellular-scale measurements for statisticalprofiling, without the need for manual proofreading. A directapplication of general-purpose segmentation and tracking algorithms isnot a viable strategy since their yield (number of nanowells analyzedwith zero errors in segmentation and tracking) is low, and theirparameter tuning needs are high.

For example, a direct application of a prior segmentation algorithm witha reported accuracy of more than 95% that is the core of the open-sourceFARSIGHT toolkit (farsight-toolkit.org) to the dataset in FIG. 2produces an error-free yield of only 43% of the nanowells for the basiccase when a nanowell contains one effector and one target (Table 1).

The situation with tracking algorithms is similar. For example, inanalyzing one sample block containing 36 nanowells, out of which 21contained at least one cell, a state-of-the art algorithm accuratelytracked xx nanowells with zero errors (yield of 28%). When the yieldfalls below 90%, burdensome manual proofreading is preferred to identifythe nanowells that were tracked accurately. If on the other hand, theautomated accuracy is at least 90%, the user can simply accept theautomated results, and the modest error that they entail.

As such, general-purpose segmentation and tracking algorithms do notexploit powerful constraints that are germane to TIMING datasets,specifically, the spatial confinement of cells, and rarity of celldivisions. They also lack mechanisms to cope with the highermorphological variability and non-uniform fluorescence of cell bodiescompared to cell nuclei that were heavily studied in the priorliterature.

In this Example, Applicants present algorithms that exploit theconfinement and cell-cycle constraints, and utilize novel segmentationapproaches to increase the yield to 98% for the basic case noted above(compare Tables 1 vs. 2), and deliver high tracking accuracies (Table3). At this level of performance, the quantitative measurements derivedfrom automated segmentation and tracking can be directly utilized forstatistical studies without the need for manual proofreading.

Example 1.1. Specimen Preparation and Imaging

The TIMING datasets were derived from ongoing studies in which human Tcells (genetically engineered to express chimeric antigen receptor CAR)and Natural Killer (NK) cells were used as effectors. Human leukemiclines NALM6, K562 or mouse EL4 cells expressing the appropriate ligandswere used as targets (T).

Both cell types were washed once in serum-free medium, suspended to ˜2million/mL and labeled with PKH67 Green and PKH26 Red dyes respectively,as directed by the manufacturer (Sigma-Aldrich). Approximately 100,000effector (E) cells were loaded onto the nanowell array, followed by˜200,000 target cells. Cells were allowed to settle into the nanowellsfor 5 minutes, and excess cells were washed away.

Next, 50 μL of Annexin V-Alexa Fluor 647 (AnnV-AF647, Life Technologies)was mixed in 3 mL of complete culture medium (RPMI-1640+10% FBS,containing no phenol red, Cellgro) and pipetted onto the nanowell arrayplate, thus immersing the array in the medium throughout the imageacquisition while allowing for nutrition and gas exchange (37° C., 5%CO₂). The nanowell array is much wider than the field of view of themicroscope. Therefore, a computer-controlled microscope stage(AxioObserver Z1, Carl Zeiss) was used to scan the array spatially.Images were collected over 12-13 hour periods at 5-10 minute intervals.This temporal sampling rate is in the range of times described betweenfirst contact and killing in previous in vitro observations. The stagemovements from one block to the next require ˜100 ms, negligiblecompared to the sampling interval.

Applicants used an LD Plan Neofluar 20×/0.4NA Korr Ph1 Ph2 M27 (CarlZeiss) objective lens combined with an optovar of 1× Tubulens, yieldinga total magnification of 200×, and a resolution of 0.325 μm/pixel (pixelsize). A Peltier-cooled (−10° C.) digital scientific CMOS camera(ORCA-Flash 4.0 V2 C11440-22CU), or Hamamatsu EM-CCD camera were usedfor recording the images.

Example 1.2. Automatic Nanowell Localization

Automatic localization of nanowells is preferred for delineating thecell confinement regions, correcting for stage re-positioning errors,and breaking up the overall TIMING dataset into a large number ofmotion-corrected video sequences, one per nanowell. Preferably, thisoperation is reliable since a single well-detection error can render thenanowell unusable for analysis, reducing the experimental yield.Preferably, the operation must also be robust to focus drift (accountingfor shrinkage/swelling/irregularity of the polymer substrate), wellswith compromised geometry, illumination variations, ringing artifacts,and debris or air bubbles that may move, and abruptly appear/disappearfrom the camera view over time (FIG. 4A).

Content-independent image registration methods like SIFT matching wereneither sufficiently reliable nor practical for TIMING data. Theyrequired multiple parameter adjustments, and failed in the presence ofartifacts. Therefore, Applicants adopted a normalized cross-correlation(NCC) based template fitting method that is robust to illuminationvariations and artifacts. Applicants exploited the fact that thegeometry of nanowells is known from the fabrication process, and theyare always visible in the phase-contrast channel.

For instance, FIG. 2B shows that some of the nanowells are intentionallyrotated by 45° for implementing a coding strategy designed to uniquelylocate individual wells in an array. Therefore, Applicants select twoempty wells (regular, and rotated by 45°) from the dataset beinganalyzed and use them as templates that are fitted to the image data.The NCC responses are in the range of (−1, +1), with −1 indicating apoor match, and +1 a perfect match.

To speed up NCC, Applicants used a Fourier implementation, and performedthe normalization in the spatial domain. FIG. 4B shows the NCC responsesof the example wells in FIG. 4A to the best-fitting (of the two)templates in FIG. 4A. Applicants used the local maximum clusteringalgorithm on the best-fitting NCC response to detect well centers, andused the known spacing between nanowells to filter out invalidresponses, yielding a robust localization of wells with more than 99%accuracy.

To cope with artifacts, Applicants discarded the nanowell videos whosemaximum NCC response falls below a predefined threshold (typ. 0.75). Theresulting rigid spatial transformation estimates (FIG. 4C) were used togenerate cropped motion-corrected nanowell video recordings.

Example 1.3. Image Pre-Processing

Each image frame of every video sequence was leveled to correctillumination variations by subtracting the local background estimated ateach pixel using a Gaussian kernel with σ=15 (FIG. 5C). Next, Applicantscorrected for spectral overlap between the emission spectra of the PKH67and PKH26 dyes used to label the effector and target cells. The columnsof the mixing matrix were estimated offline using principal componentanalysis (PCA) for a 7×7 block of nanowell videos, and then re-usedacross the rest of the array. The unmixing was performed by a linearinverse method.

Finally, Applicants smooth the images using a median filter with aradius r_(m)=3 while preserving cell boundaries. As noted by otherauthors, such pre-processing is preferred for reducing high-throughputcell segmentation errors.

Even after pre-processing, cells exhibit variability in shape andintra-cellular fluorescence (FIG. 6A), and this is a challenge for celldetection and separation of touching/overlapping cell bodies. The widelyused multi-scale Laplacian of Gaussian (LoG) map misses the dim cellindicated by the red arrow, and is unable to separate the pair of cellsindicated by the yellow arrow that exhibit non-round shapes andnon-uniform intensities (FIG. 6C). Moreover, gradient-weighted watershedalgorithms have difficulty separating cells with weak edges.

In order to overcome the aforementioned limitations, Applicants proposea normalized multi-threshold distance map (NMTDM) (FIG. 6C) that isdesigned to detect cell bodies that works as follows. The normalizedpixel intensity distributions p(i) of Applicants' pre-processed imagesare modeled by a mixture of 3 Gaussian distributions in accordance withEquation 1.

p(i)=Σ_(k=1) ^(K) w _(k) g(i|μ _(k),σ_(k))  Eq. 1

In Equation 1, parameters (μ_(k),σ_(k)) and weights w_(k), k=1, 2, 3capture the dim background, intermediate foreground, andhyper-fluorescent foreground pixels, respectively. Applicants used thek-means algorithm with deterministic seeding for estimating the mixtureweights since it is fast, requires few initialization parameters,converges reliably, and produces comparable results to expensiveexpectation maximization algorithms, making it ideal for Applicants'high-throughput analysis.

Clusters 2 & 3 together capture the image foreground, where L_(min) andL_(max) denote the minimum and maximum pixel intensity values for thisforeground. Applicants define a series of M threshold values (typ. 20)denoted l between L_(min) and L_(max) separated byδ=(L_(max)−L_(min))/M, where l=L_(min), L_(min)+δ, L_(min)+2δ, . . . ,L_(max). Each of these thresholds is used to generate a correspondingbinary mask denoted B_(l)(x,y) and a corresponding Euclidean distancemap D_(l)(x,y). Each of these binary masks are subjected to connectedcomponents analysis, yielding a set of connected components denotedR_(h), h=1, . . . , H.

Next, Applicants normalized the Euclidean distance maps for eachconnected component by the corresponding maximum value within R_(h), toensure that the distance maps at different levels contribute equally tothe final response. With this, the normalized multi-threshold distancemap (NMTDM) for a connected component R_(h) can be written in accordancewith Equation 2.

$\begin{matrix}{{R\left( {\left( {x,y} \right) \in R_{h}} \right)} = {\frac{1}{M} \times {\sum\limits_{l}\frac{D_{l}\left( {\left( {x,y} \right) \in R_{h}} \right)}{\max\limits_{({x,y})}{D_{l}\left( {\left( {x,y} \right) \in R_{h}} \right)}}}}} & {{Eq}.\mspace{14mu} 2}\end{matrix}$

The NMTDM exhibits clear peaks, one per cell, unlike the multi-scale LoG(FIG. 6C vs. 6C). Moreover, local maxima clustering over this map yieldsa more reliable estimate of the cell count than the multi-scale LoGfilter. This step requires only two parameter settings: the number oflevels M and the clustering radius r for selecting the peaks. Usingthis, Applicants estimate the number of cells independently for eachframe, and compute a histogram over the time series (FIG. 6D). Knowingthat the number of cells in a nanowell stays constant (cell divisionsare rare within the observation period), the presence of more than onenon-zero entry in this histogram implies that conventional segmentation& tracking results would be error prone for this nanowell, and requireproofreading. However, the histogram exhibits a peak at the correct cellcount.

Applicants found that the histogram peak is a reliable indicator of cellcounts over a time-lapse sequence, despite errors in individual frames.Moreover, the height of the peak of the normalized histogram is areliable measure of Applicants' confidence in the cell count. For thisillustration, the peak reaches 82%. Applicants discard nanowells forwhich the peak falls below 75%.

Example 1.4. Confinement Constrained Cell Re-Segmentation

Although the above-described method is effective for estimating thecorrect number of cell bodies, it does not yield precise cell locationestimates and cell segmentations because it assumes that the cells arebrighter closer to their centers. Applicants' strategy to overcome thislimitation is to use the histogram-based cell count estimate tore-segment the cells de novo by a normalized spectral clustering ofimage pixels. This method can detect cells of diverse shapes, and tendsto estimate clusters (cells) with similar sizes—a reasonable assumptionwhen handling ambiguous images.

Given N foreground pixel coordinates {X_(i)}_(i=1, . . . , N),Applicants compute a similarity matrix W∈R^(N×N) in accordance withEquation 3.

$\begin{matrix}{{W\left( {i,j} \right)} = \left\{ {\begin{matrix}{{\exp \left( {- \frac{{{x_{i} - x_{j}}}^{2}}{2\sigma^{2}}} \right)},} & {{{{if}\mspace{14mu} {{x_{i} - x_{j}}}} < ɛ};} \\0 & {otherwise}\end{matrix},} \right.} & {{Eq}.\mspace{14mu} 3}\end{matrix}$

In Equation 3, c is a user-defined constant representing the maximumdistance between a pixel and its neighbors, Ll is the Euclidean norm,and σ controls the neighborhood width. Next, Applicants compute thedegree matrix D and the un-normalized graph Laplacian matrix L=D−W,where D is a diagonal matrix defined in Equation 4.

D(i,j)=Σ_(i=1) ^(N) W(i,j).  Eq. 4

In Equation 4, Applicants form the matrix U=[u₁, . . . , u_(K)]∈R^(N×K)by computing the first K eigenvectors u₁, . . . , u_(K) of thegeneralized eigenvalue problem Lu=λDu. Finally, Applicants cluster thepoints {y_(i)}_(i=1, . . . , N) corresponding to the rows of U intoclusters C_(i), i=1, . . . , K, and re-label the foreground pixels{x_(i)}_(i=1, . . . , N) accordingly. This method (FIGS. 6E-G) enablescells in video sequences to be re-segmented accurately, despite errorson individual frames.

Applicants are guaranteed to obtain a fixed number of cells across eachmovie, and this simplifies cell tracking. Interestingly, the confinementconstrained cell re-segmentation algorithm also enables efficientediting of incorrect segmentation & tracking results. If needed, a usercan re-run the spectral clustering based re-segmentation with thecorrected cell count, and this yields the correct results in most cases.

Example 1.5. Confinement-Constrained Cell Tracking

Reliable cell tracking is needed to quantify the complex motilebehaviors of cells at high throughput. The low temporal sampling rate(5-10 minutes/frame) implies that cells can undergo significantdisplacements and shape changes between frames. In addition, the effectof the nanowell walls makes it difficult to predict cell movements.Importantly, Applicants wish to avoid the need for manual proofreading.

With these considerations in mind, Applicants propose aconfinement-constrained tracking method that is fast, fully automated,and reliable. It is formulated globally over the entire movie, ratherthan on a successive frame-by-frame basis. It does not require anyinitialization, and requires only 3 parameters. Note that Applicants'algorithm is not intended for general-purpose cell tracking problems.Rather, it is designed specifically for confinement-constrained data.For general problems, sophisticated cell-tracking methods have beendescribed and compared in the literature. The approaches includeparticle filtering, Kalman filtering that require a motion model and anobservation model, but do not need prior segmentations. Contour based,mean-shift, and level-set methods, are preferable when high-temporalresolution data are available, and some can handle merging and splittingof cells implicitly. Optimization-based approaches require objects to bedetected/segmented a priori and are preferred for low temporalresolution data. When objects can enter/exit the field, cells divide ordie, or when the segmentation is unreliable, elaborate methods aredescribed to handle appearance, disappearance, merge and split, andautomatic correction of segmentation errors. For TIMING data, Applicantsare unconcerned with such complications because of the nanowellconfinement property. Therefore, Applicants' formulation is astreamlined formulation.

Applicants formulated the tracking of K cells over T frames as aglobally optimal edge selection problem on a directed graph. A noden_(j) ^(t) in the graph represents cell j at frame t, and is describedby an attribute vector d_(j) ^(t)={c_(j) ^(t),a_(j) ^(t),r_(j) ^(t)},where c_(j) ^(t)=(x_(j) ^(t),y_(j) ^(t)) is the centroid; a_(j) ^(t) isthe area of the cell; and r_(j) ^(t) denotes the pixels defining cell j.An edge e_(i,j) ^(t)={n_(i) ^(t-1),n_(j) ^(t)} associates cell i atframe t−1 to cell j at frame t, and Applicants compute an associationcost φ_(i,j) ^(t) that measures the dissimilarity between cell regions iand j. An edge selection variable γ_(i,j) ^(t)∈{0,1} indicates if agiven edge is selected in the final solution. Using integer programming,Applicants seek the solution γΣ{0,1}^(N), where N=(T−1)×K×K thatminimizes the following sum of association costs over each nanowell.

$\begin{matrix}{{\Gamma = {\underset{\gamma \in {\{{0,1}\}}^{N}}{\arg \; \min}{\sum\limits_{t = 2}^{T}{\sum\limits_{j = 2}^{K}{\sum\limits_{i = 1}^{K}{\phi_{i,j}^{t}\gamma_{i,j,}^{t}}}}}}}{s.t.\mspace{14mu} \left\{ \begin{matrix}{{\sum\limits_{i = 1}^{K}\gamma_{i,j}^{t}} \leq 1} & {{for}\mspace{14mu} \begin{matrix}{{j = 1},\ldots \mspace{20mu},K,} \\{{t = 2},\ldots \mspace{14mu},{T.}}\end{matrix}} \\{{\sum\limits_{k = 1}^{K}\gamma_{i,k}^{t + 1}} \leq 1} & {{for}\mspace{14mu} \begin{matrix}{{j = 1},\ldots \mspace{20mu},K,} \\{{t = 2},\ldots \mspace{14mu},{T - 1.}}\end{matrix}}\end{matrix} \right.}} & {{Eq}.\mspace{14mu} 5}\end{matrix}$

The cell confinement constraint is implicit in Equation 5. Theinequality constraints ensure that each node n_(j) ^(t) is associatedwith a maximum of one node in the previous frame, and the next frame,respectively. In computing φ_(i,j) ^(t), Applicants ignore shape andtexture features since cell morphologies and intensity profiles varyover time. Applicants compute a weighted sum of the Euclidean distancebetween cell centroids g(c_(i),c_(j)), the area difference between cellsg(a_(i),a_(j))=|a_(i)−a_(j)|, and the set-theoretic distance between thepixels (r_(i),r_(j)) for the two cells described in Equation 6.

$\begin{matrix}{{g\left( {r_{i},r_{j}} \right)} = \left\{ \begin{matrix}{1 - \frac{a_{overlap}}{\min \left( {a_{i},a_{j}} \right)}} & {{{if}\mspace{14mu} v_{overlap}} > 0} \\{{\min \mspace{14mu} \left( {{dist}\left( {r_{i},r_{j}} \right)} \right)},} & {{otherwise},}\end{matrix} \right.} & {{Eq}.\mspace{14mu} 6}\end{matrix}$

In Equation 6, a_(overlap) is the overlapping area, andmin(dist(r_(i),r_(j))) is the shortest distance between the cells'pixels. The overall cost is written asφ_(i,j)=w₁×g(c_(i),c_(j))+w₂×g(a_(i),a_(j))+w₃×g(r_(i),r_(j)), where theweights w₁, w₂, and w₃ can be adjusted if needed. Applicants used thedefault values w₁=1, w₂=10, and w₃=100. One can increase w₃ when hightemporal resolution data and good segmentation results are available.Applicants solve the integer program in Equation 5 by using thebranch-and-bound algorithm. Although the theoretical worst-case runningtime can grow exponentially, this is not a concern since Applicants areprocessing small cohorts of cells in each nanowell.

FIG. 7A illustrates the results of automated tracking for a nanowellcontaining only effector cells, showing our ability to cope with largeinter-frame movements. Panels B-E depict sample cell trajectories foreffector and target cells with diverse motion patterns. Additionalexamples are presented in FIG. 9.

Example 1.6. Detection and Quantification of Cell-Cell Contacts

Detecting contacts between effectors and targets, and measuring thecontact parameters (e.g., onset time, duration, frequency, extent) isneeded for understanding how cell behaviors predict subsequent events ofinterest, especially the killing of targets by effectors. Approachesusing the spatial proximity of cell segmentations can be unreliable forTIMING data since they require much higher resolution imaging, and aresensitive to segmentation errors. With this in mind, Applicants define asoft cell interaction measure CI for quantifying the interaction of acell with its surrounding cells, as follows.

First, Applicants compute the normalized effector fluorescence signalI_(N) ^(j)(x,y) in each nanowell j. Next, Applicants define a series ofring-like compartments using a Euclidean distance map D(x,y) withrespect to the segmented target cells, as illustrated in FIG. 8. Pixelswith distances between k and k+1 pixels form compartments b_(k) withinner radii k={1, 2, . . . , n}, where n is the maximum distance neededto cover the complete nanowell. Applicants sum the fluorescenceintensities over each compartment, and normalize them by their radii kthat are proportional to the compartment areas. With this, the cellinteraction measure CI(t) is the following weighted intensity summationin Equation 7.

$\begin{matrix}{{{CI}(T)} = {\sum\limits_{k = 1}^{n}\left\lfloor {\frac{1}{k}{\sum\limits_{{({x,y})} \in b_{k}}{I_{N}^{j}\left( {x,y} \right)}}} \right\rfloor}} & {{Eq}.\mspace{14mu} 7}\end{matrix}$

FIG. 8 shows how CI(t) captures cell contacts in a graded manner over animage sequence. This measure can be thresholded to detect cell contactevents with a desired sensitivity. The threshold is set and verifiedmanually by an immunologist based on visual verification of at least 30nanowells for each TIMING dataset, starting with a default value (typ.0.01). Applicants consider this visual verification to be valuable duediligence. A full manual annotation of the contacts by five independentobservers over 156 nanowell videos showed a 90.4% concordance with thedefault threshold. In order to definitely assign contact, it is definedto occur when the threshold criterion is met for two successive frames.CI(t) is defined above for a single cell and its neighbors. It can beextended to handle multiple cells by using segmentation masks.

Example 1.7. Feature Computation

For each cell, the automated segmentation and tracking operationsproduce multiple time series of primary features including cell location(x,y), area a(t), instantaneous speed v(t), cell shape as measured bythe eccentricity of the best-fitting ellipse e(t), and the contactmeasure CI(t). In addition, target cell death events (apoptosis) aredetected using Annexin V, whose summed fluorescence intensity i_(d)(t),is measured as another primary feature. Next, Applicants computecellular features at the scale of each nanowell, specifically, thenumber of effector cells n_(e), target cells n_(t), dead effectorsn_(ed), contacted targets n_(tc), and killed targets n_(tk). Thesemeasurements can be used to profile the nanowells.

The primary cellular features capture important aspects of the cellularactivities within each nanowell, but they have two disadvantages. First,they have a variable dimensionality, since the number of time pointsvaries across TIMING experiments. Second, a long experiment can resultin unnecessarily high dimensional feature data. With the intent ofderiving meaningful lower-dimensional representations of cellular eventsindependent of the number of time points, Applicants derive a set ofeight secondary features for each cell. For each cell, Applicantscompute the average speed prior to first contact v _(free), averagespeed during the contact phase v _(contact), average cell eccentricityprior to first contact ē_(free), average eccentricity during the contactphase ē_(contact), time elapsed between first contact and death Δt_(d),total contact duration between first contact and death Δt_(cd), timeduration before first contact Δt_(free), and the number of conjugationsprior to target cell death n_(cd).

Example 1.8. Experimental Results

The proposed method was evaluated on 12 TIMING experiments involvingcombinations of target cells (NALM6, K562, and EL4) and effector cells(NK cells or CART cells), to evaluate its ability to cope withbiological and imaging variability, different cell types, experimentaldurations, and changes in instrumentation. All the datasets wereanalyzed using the parameter settings summarized in Table 4.

TABLE 4 List of parameter settings. Parameter Processing Step Value(units) Median Filter Size Preprocessing 3-5 (pixels) r_(m) Local Max.Nanowell Well width Clustering 

Detection (pixels) NCC Response Nanowell 0.75 (on 0-1 scale) ThresholdDetection Number of Mixtures K Binarization 3 Thresholding Levels M SeedDetection 20 (levels) Local Max. Seed Detection 8-15 (pixels)Clustering 

Neighborhood ϵ Spectral 2 (pixels) Clustering Shape Parameter σ Spectral2 (pixels) Clustering Cost Weights Cell Tracking 1, 10, and 100 w₁, w₂,w₃ CI threshold Contact 0.01 Analysis Death marker Cell Death 150threshold Analysis

Given the sheer volume of the data, Applicants start by presenting avisual summary of sample segmentation and tracking results in FIG. 9.Overall, the algorithms and parameter settings proved reliable forautomated analysis. As a detailed example, Applicants present results ofmanually validating the segmentation and tracking on a small datasetwith 2,000 nanowells containing CAR⁺T cells and NALM6 cells imaged over130 time points. Of these, 157 nanowells had >4 effectors or targets.These larger cohorts are irrelevant for the current biological study ofinterest, so they were omitted from further analysis. An additional 33wells (1.7%) were discarded because the confidence in cell counts(histogram peak) was below 75%. Of the remaining 1,803 nanowells, only 7were not detected accurately due to air bubbles, so Applicants' overallnanowell detection accuracy exceeded 99%.

Example 1.9. Improvement in Yield

In order to assess the fraction of wells with zero cell detectionerrors, Applicants manually validated the results over the 1,803remaining nanowells using the proposed method and a prior algorithm as abenchmark. The results are summarized in Table 2.

For the table entries with more than 90 wells, Applicants manuallyvalidated 40% of wells, and the full set of wells for the remainingentries. Comparing the corresponding entries in Tables 1 & 2 shows thatthe proposed method dramatically increased the number of usable wells.The few errors were due to persistently dim fluorescent cells that weremissed, or because a cell was persistently occluded by another cell formore than 80% of the recording duration. For perspective, yield rates ofless than 90% render the automated image analysis results unusable,since the user has to manually analyze an excessive number of nanowells.With a yield close to 98%, the user can simply accept the automatedresults, and the modest error that they entail.

Example 1.10. Cell Segmentation and Tracking Performance

Nearly 5,061 cells were segmented and tracked in this dataset. Theautomated segmentation and tracking results were overlaid on the moviesand presented to an immunologist, and the errors were scored as:under-segmentation; over-segmentation; and incorrect association.Over-segmentation errors appear when a cell is identified as two or moreobjects. Under-segmentation occurs when the same label is assigned tomultiple cells. Both of these errors can occur if the cell count isincorrect. Incorrect correspondence occurs when the tracking fails,usually due to segmentation errors.

Applicants consider a single association error sufficient to render thetracking results for a nanowell movie unusable. Despite this stringentrequirement and the high volume of data, the algorithm is extremelyaccurate (Table 3).

TABLE 3 Frequency of cell segmentation and tracking errors. Number ofcells 2 3 Targets Targets 4 Targets Under-segmentation 1.2% 1.4% 0.1%Over-segmentation 1.3% 0.6% 4.2% Incorrect- 0.9% 0.8% 3.6%correspondence Total cells validated 816 636 168

Next, Applicants compared automatic segmentations of 30 randomlyselected target and effector cells against manual segmentations. TheJaccard similarity index for target cells was 0.86±0.12 (mean±std.) and0.78±0.17 for effector cells, indicating good segmentation accuracy.

Example 1.11. Data Analysis

Applicants analyzed a TIMING dataset containing 11,520 nanowells (320blocks of 6×6 wells) in which Applicants imaged the dynamics of killingof K562 cells by in-vitro expanded NK cells for 8 hours at 6 minuteintervals. From the automatically extracted features, Applicantsselected only the nanowells containing exactly 1 effector and 1 targetcell, showing a stable effector-target contact of at least 6 minutes (2successive frames), and in the case of target death, contact by effectorprior to death. This resulted in a cohort of 552 nanowells that is idealfor analyzing the dynamic behaviors of effectors, without the confoundsassociated with multi-effector cooperation or serial killing.

Comparisons of the out-of-contact motility and the velocity during tumorcell conjugation demonstrated that NK cells that participated in killingdisplayed higher motility during both phases (FIG. 10A), consistent withApplicants' recent report that demonstrated that motility might be abiomarker for activated immune cells (see Example 2). Furthermore, forNK cells, the change in speed and arrest upon target cell ligation iswell-documented.

Second, Applicants were interested in quantifying differences in NK cellbehavior in interacting with live or dead cells. Of all the NK cellsthat participated in killing, only 18% re-conjugated to target cellssubsequent to apoptosis, and when they did, their duration ofconjugation of 18±14 minutes was significantly shorter than conjugationsmediated by the same NK cells to live tumor cells (52±72 minutes) (FIG.10B). These results suggest that NK cells largely avoided conjugating todead target cells, and even when they did, made an early decision toterminate the conjugation.

Example 1.12. Implementation

For a block with 36 wells and 60 cells, the processing time is 9-10seconds/block per time point on a Dell 910 PowerEdge server with 40 CPUcores, 1 TB of RAM, and a RAID 5 storage system. The cell tracking took1.1 secs/block. Segmentation took 3.1 secs/block. Well detection took1.5 secs/block. Feature computation took 3.5 secs/block. The algorithmswere implemented in Python & C++, except for the spectral clusteringthat used a compiled MATLAB executable.

Example 1.13. Conclusions

The combined TIMING system consisting of the nanowell arrays andApplicants' automated confinement-constrained image analysis methodsenable a far more comprehensive sampling of cellular events than ispossible manually. The proposed algorithms dramatically improved theyield and accuracy of the automated analysis to a level at which theautomatically generated cellular measurements can be utilized forbiological studies directly, with little to no editing. Mostsegmentation and/or tracking errors (mostly due to persistently lowfluorescence, or occlusion over extended durations) can be detectedbased on the confidence metric, and the corresponding nanowells caneither be ignored or edited. Applicants' method is scalable tomulti-terabyte TIMING datasets, and does not require elaborateinitialization or careful parameter tuning.

Example 2. Integrated Single-Cell Functional and Molecular Profiling ofDynamic T Cell Behavior

In this Example, Applicants demonstrate the development and validationof a scalable single-cell methodology that integrates responses basedupon microbead molecular biosensors for detecting protein secretion,automated time-lapse microscopy to monitor cell motility and cell-cellinteractions, and microfluidic quantitative polymerase chain reaction(qPCR) for highly multiplexed transcriptional profiling. Analysis of1,178 single tumor-reactive T cells interacting with 3,122 tumor targetcells over a period of 5 hours revealed that the integrated behavior ofpolyfunctional T cells having both target killing and IFN-γ secretionwas similar to that of serial killers without IFN-γ secretion. Thissuggested that cytolysis was the dominant determinant of the interactionbehavior and that killing enables faster synapse termination.

In particular, Applicants have validated an integrated methodology thatcombines microbead-based molecular sensors for detecting cytokinesecretion from single T cells concurrently with Timelapse Imaging InNanowell Grids (TIMING) to monitor T-cell motility and cytotoxicity,without the need for encapsulation. TIMING was used to combinefunctional and molecular screening at the single-cell level, byperforming multiplexed transcriptional profiling (96 genes) onCD19-specific CAR⁺ T cells. Simultaneous quantification of theinteraction between individual tumor-specific CD8⁺ T cells and multipletarget cells demonstrated that IFN-γ was the most common functionelicited. However, CD8⁺ T cells with killing ability, especially serialkilling ability, required shorter durations of target cell conjugationin comparison to IFN-γ secreting mono-functional cells, indicating rapidsynapse termination by T cells capable of killing versus cytokinesecretion. The behavioral interaction of polyfunctional T cellsexhibiting both killing and IFN-γ secretion was similar to that ofserial killers without IFN-γ secretion, suggesting that killing was thedominant determinant of the interaction behavior.

Tracking the velocities of these cells by longitudinal time-lapseimaging revealed that these serial killer T cells (with or without IFN-γsecretion) may be identified based on their higher out-of-contact basalmotility. Single-cell multiplexed transcriptional profiling of T cellsidentified only by their basal motility, confirmed that the motile cellsexpressed an activated phenotype with significantly increased amounts ofperforin and other genes associated with chemotaxis.

Without being bound by theory, Applicants propose an integrated model offunctional CD8⁺ T-cell behavior based on these results. Moreover, theseresults establish Applicants' methodology as an investigational tool forcombining multiplexed functional and molecular screening at thesingle-cell level, and suggest that motility might be a surrogatebiomarker for identifying T cells with killer phenotype which haspotential implications for immunotherapy.

Example 2.1. Design of an Integrated Platform for Simultaneous Profilingof Protein Secretion and Dynamic Cell-Cell Interactions

In this Example, Applicants designed an integrated method that had theability to add or remove independent modules in determining thepolyfunctional nature of the T cells: cytokine secretion, dynamics ofinteraction with target cells, cytotoxicity, and molecular profiling(FIG. 11). Starting with Applicants' recently reported TIMING assay(Example 1), Applicants implemented functionalized beads as biosensorsof the local microenvironment within individual nanowells to profilecytokine secretion (FIG. 12A), and microfluidic qPCR to facilitate geneexpression profiling. This integrated approach could thus be used toprofile cytokine secretion simultaneously with cytotoxicity, on oneunified microscope platform.

Example 2.2. Frequency of IFNγ-Secreting T Cells Enumerated byFunctionalized Microbeads within Nanowell Arrays is Correlated to theSame Responses Determined Using ELISpot

Applicants first tested the ability of functionalized microbeads toefficiently capture proteins secreted by single cells after incubationin individual nanowells by measuring the limit of detection (LoD) offunctionalized beads at different concentrations of the analyte.Briefly, antibody-coated beads were incubated with varyingconcentrations of IFN-γ (0-5000 pg/mL) for a period of two hours at 37°C., loaded onto nanowell arrays, and subsequently detected with afluorescently labeled secondary antibody. The background corrected meanfluorescent intensity (MFI) quantified across a minimum of 30 beadsconfirmed that IFN-γ was detectable at a concentration of 500 pg/mL(FIG. 12B).

Next, the correlation between the nanowell encapsulated bead assay andELISpot for quantifying frequencies of single T cells secreting IFN-γupon activation was determined. To account for variations in stimulusand the diversity of T-cell populations, the frequency of IFN-γsecreting single T cells was enumerated under three sets of conditions:stimulation of peripheral blood mononuclear cells (PBMC) with HLA-classI peptide pools targeting common viral antigens; stimulation of PBMCwith phorbol 12-myristate 13-acetate (PMA)/ionomycin; and stimulation ofin vitro expanded, melanoma-specific TIL with PMA/ionomycin. An aliquotof 10⁶ cells were stimulated for a period of 3-5 hours and an aliquot of˜100,000 cells was loaded onto a nanowell array (84,672 nanowells, 125pL each). A suspension of 200,000 beads pre-coated with anti-IFN-γ wassubsequently loaded onto the nanowell array and incubated for a periodof 2 hours at 37° C. By analyzing an average of 10,182±8,589 (mean±s.d.)single cells matched to one or more beads within the nanowells, thefrequency of the activated T-cell IFN-γ response was determined to be0.40-7.8%. The magnitude of these responses were similar to thoserecorded by ELISpot [0.20-11.2%], and results of both assays weresignificantly correlated (r²=0.87, p-value=0.0008), demonstrating thatbeads can be efficiently utilized to capture cytokine secretion fromsingle cells (FIG. 12C). In the absence of stimulation, the frequency ofIFN-γ beads detected when incubated with T cells was <1 in 10,000 andthis set the limit of detection of our assay at 0.01%.

Example 2.3. In Open-Well Systems, Fractional Occupancy of Analyte onBeads Increases as the Density of the Antibody Used to Capture AnalyteDecreases

As opposed to encapsulated systems, open-well configurations can beadvantageous for the long term monitoring of cell fate and functionsince they allow continuous exchange of gases and nutrients.Furthermore, they avoid potential alterations of cellular behavior thatcan arise from the artificially high local concentrations of analytescommonly found in closed systems.

A disadvantage of open-well systems is that the analyte secreted by anindividual cell within a nanowell is subjected to persistent diffusioninto the bulk medium, potentially lowering the sensitivity. Therefore,Applicants sought to quantify the efficiency of analyte capture on beadsby modeling a simplified open-well system using finite elementsimulations (FIG. 13A). The concentration of analyte in liquid media (C)can be described using Fick's 2^(nd) law, as illustrated in Equation 8.

$\begin{matrix}{\frac{\partial C}{\partial t} = {D{\nabla^{2}C}}} & {{Eq}.\mspace{14mu} 8}\end{matrix}$

In Equation 8, D represents the diffusion coefficient of the analyte.Since the walls of the PDMS can be assumed to be largely impermeable toproteins, the flux at these boundaries was set to zero. At a constantrate of analyte secretion from the cell (10 molecules/seconds), the massbalance of analyte concentration on bead surface (C_(s)) was determinedby Equation 9.

$\begin{matrix}{\frac{\partial C_{s}}{\partial t} = {{D_{s}{\nabla^{2}C_{s}}} + {k_{on}{C\left( {\theta_{0} - C_{s}} \right)}} - {k_{off}C_{s\;}}}} & {{Eq}.\mspace{14mu} 9}\end{matrix}$

In Equation 9, D_(s) represents diffusivity of analyte on bead surface,k_(on) and k_(off) represent kinetic binding constants determined bystrength of capture antibody-analyte interaction and θ₀ representsnumber of capture antibodies available per unit surface area of thebead. The choice of parameter values (FIG. 13A) was based oncommercially available antibody binding affinities, the known rates ofcytokine secretion from T cells, and previously reported numericalsimulations of closed systems. Initial concentrations of analyte inliquid media and bead surface were set to zero and increase infractional occupancy

$\left( {∯\frac{C_{s}}{\theta_{0}}} \right)$

of the bead with time as the cell secretes the analyte was modeled.

Upon validating the model with previously published data, Applicantssought to optimize two key tunable variables, the size of beads and thesurface density of capture antibodies to maximize fractional occupancy(and therefore the fluorescent pixel intensity). The simulationsdemonstrated that the fractional occupancy of all three bead sizesincreased linearly as a function of time (1-6 hours), and thatregardless of the incubation time, the 3 μm bead had a 1.8-fold and2.7-fold higher fractional occupancy in comparison to the 5 μm and 7 μmbeads (FIG. 13B).

When the bead diameter was held constant (3 μm), but the binding sitedensity was varied across three orders of magnitude, the beads with thelowest binding site density (10⁻⁹ mol/m²) had the highest fractionaloccupancy (FIG. 13C). These results show that increased fractionaloccupancy is observed when the total number of binding sites isdecreased by either decreasing the bead size, or binding site density,and are consistent with ambient analyte theory that predicts that highersensitivity can be achieved by lowering the number of antibodies used tocapture the analyte.

Furthermore, for a nanomolar binder at low fractional occupancy(neglecting desorption), the simulations predicted that the kinetics ofanalyte capture is diffusion limited (FIG. 13A), in agreement withprevious studies on antibody microspots, closed-well systems, andtwo-compartment mathematical models. It should however be noted that,unlike microspot assays, the present system does not conform to ambientanalyte conditions as depletion of analyte by capture on the beadsurface is not negligible in comparison to total analyte available.

Example 2.4. Simultaneous Quantification of Cytotoxicity and IFN-γSecretion in Tumor-Specific CD8⁺ CAR⁺ T Cells Using TIMING

Since the end-point experiments confirmed the ability to detect IFN-γfrom single T cells upon activation, and the modeling suggested that thebeads should work well in an open-well system, Applicants integrated thebeads into the TIMING workflow to enable measurement of effector targetinteractions while also capturing any secreted IFN-γ protein, atsingle-cell resolution.

Applicants chose to interrogate the polyfunctionality of tumor-specificindividual CD8⁺ T cells with regards to cytokine secretion andcytotoxicity. Genetically modified and propagated T cells were generatedfrom the peripheral blood mononuclear cells (PBMC) of a healthy donor toenforce expression of a second generation CD19-specific CAR (designatedCD19RCD28) that activates T cells via a chimeric CD3 and CD28 endodomain(FIG. 14). Subsequent to numeric expansion on activating and propagatingcells (AaPC) for a period of four weeks, the CAR⁺ T cells werepredominantly CD8⁺ (>99%, FIG. 15A). Phenotypic characterization of theCD8⁺ CAR⁺ T cells demonstrated that the dominant subset of T cells werenaïve like (CD45RA⁺CD62L⁺, 60.7%, FIG. 15B). The ability of these Tcells to specifically secrete IFN-γ upon interaction with cellspresenting CD19 antigen was confirmed by co-incubating with both NALM-6tumor cells (CD19 positive) and EL4 cells (CD19 negative, FIG. 15C).

CAR⁺ T cells as effectors, NALM-6 tumor cells as targets, andpre-functionalized beads coated with IFN-γ capture antibody as cytokinesensors, were loaded sequentially onto a nanowell grid array.Effector-mediated tumor lysis was detected using Annexin V staining andevery individual nanowell (14,400 wells, 64 pL each) was profiled for aperiod of 5 hours (FIG. 16A), and cytokine secretion was quantified bythe formation of immune-sandwiches on beads (FIG. 16B).

Applicants modified previously-reported image analysis algorithms to notonly enable the automated segmentation and tracking of cells, but to nowfacilitate the identification of fluorescence intensity on the beads toreport on the secretion of IFN-γ. After a simple diameter-based gating,Applicants identified 1,178 wells of interest containing a single Tcell, 2 to 5 tumor cells, and one or more beads. Nanowells containingmultiple tumor cells were specifically chosen to allow observation ofindividual T cells participating in multiple killing events. Within thissubset, since every T cell was incubated with multiple tumor cells,three separate functional definitions were employed: serial killer cellsthat killed at least two tumor cells, mono-killer cells that killedexactly one tumor cell, and IFN-γ secreting cells.

Subsequent to conjugation to one or more tumor cells, IFN-γ secretionwas the most commonly observed function recorded in single T cells(64.2%, FIG. 17A). Polyfunctional cells defined as either CAR⁺ T cellsthat killed multiple tumor cells (44.1%) or cells that were able to killat least one tumor cell and simultaneously secrete IFN-γ was onlyslightly lower (53.6%, FIG. 17A). The subset of cells capable of bothmulti-killing and IFN-γ secretion comprised 30% of the population.

Example 2.5. Killer CAR⁺ T Cells Detach Faster from Target Cells inComparison to IFN-γ Secreting Cells

Since TIMING assays, as described above, have the ability to monitorboth conjugate formation and functional readouts, and since the CD8⁺ Tcells uniformly expressed the high-affinity immunoreceptor, Applicantsquantified the threshold for activation by analyzing the total durationof conjugation prior to functional readout. T cells that only secretedIFN-γ (monofunctional), exhibited the longest conjugation durations ofall functional T cells (159±8 min). This duration was significantlylonger than cells that killed either only one tumor cell with (94±5minutes) or without IFN-γ (89±6 minutes) secretion, or multiple tumorcells with (74±2 minutes) or without IFN-γ (79±4 minutes) (FIG. 17B).

These results suggest that the duration of conjugation between T cellsand tumor cells that results in killing has a lower threshold forfunctional activation in comparison to IFN-γ (monofunction). To definethe kinetics of the interaction between individual T cells and tumorcells that lead to subsequent killing, two interaction parameters,t_(Contact), cumulative duration of conjugation between first contact totarget death; and t_(Death), time between first contact and targetapoptosis, were computed (FIG. 18). The t_(Contact) parameter reflectsthe duration of stable conjugation and t_(Death) reflects the kineticsof target apoptosis.

For both mono-killers and serial killers, t_(Contact) was significantlylower than t_(Death) demonstrating that T cell detachment precededtumor-cell Annexin V staining (FIG. 19). Second, the total duration ofconjugation of all killer T cells (81±2 minutes) was lower thannon-killer T cells (154±6 minutes) (p-value<0.0001, FIG. 19B).

The aforementioned results suggest that, at the single-cell level, therelationship between exact time at which single T cells terminate thesynapse and time of target cell apoptosis is heterogeneous. Inaggregate, killer T cells terminated the synapse upon initiation ofkilling but prior to appearance of the apoptosis markers on tumor cells.

No significant differences were observed in the t_(Contact) whencomparing serial killer CAR⁺ T cells, with or without IFN-γ secretion(FIG. 17C), suggesting that killing is the dominant behavior indetermining duration of conjugation. The frequency of individual serialkiller T cells that either secreted IFN-γ (353/1178=30%) or did notsecrete IFNγ (166/1178=14%) was not significantly different from T cellsthat only secreted IFN-γ (147/1178=12%) (Fisher 2×2 test, p-value=0.2)confirming that shorter duration of conjugation still providedsufficient activation for cytokine secretion.

Next, Applicants compared mono-killers and serial killers, with andwithout concomitant IFN-γ secretion, measured by t_(Contact) andt_(Death). In order to facilitate direct comparisons, each of thetargets killed by the serial killer T cells was sorted based on theorder in which they made contact with the effector cell. In the absenceof IFN-γ secretion, serial killer effector cells showed no significantdifferences in either t_(Contact) (69±5 minutes) or t_(Death) (94±6minutes) in killing of the first target encountered, in comparison tomono-killers (t_(Contact): 89±6 minutes, t_(Death): 117±7 minutes, FIGS.17C-D).

In contrast, serial killer effector cells that also secreted IFN-γshowed a decreased duration of conjugation (t_(Contact): 68±3 minutes)and an increased efficiency of killing (t_(Death): 93±4 minutes) inkilling of the first target encountered, in comparison to mono-killersthat secreted IFN-γ (t_(Contact): 94±5 min, t_(Death): 121±5 min). Thisdifference was only observed for the first target since subsequenttargets killed by the serial killers did not show significantdifferences in either t_(Contact) or t_(Death) (FIGS. 17C-D). Insummary, these results showed that polyfunctional T cells that are ableto participate in both serial killing and secrete IFN-γ, have a lowerthreshold for the duration of activation prior to a functional response.

Example 2.6. Basal Motility when not in Target Cell Contact May be Usedto Identify Serial Killer Polyfunctional CAR⁺ T Cells

Next, Applicants investigated if intrinsic T-cell behavioral parameterslike basal motility (d_(Well): average mean displacement within thenanowell over 5 minute periods) prior to tumor cell conjugation, mightoffer insights into their functional capacity subsequent to tumor cellconjugation. Individual CAR⁺ T cells that failed to display anyfunctionality (killing/IFN-γ secretion) upon tumor cell conjugation alsohad the least out-of-contact motility (d_(Well): 1.3±0.1 μm) of the Tcells subgroups profiled (FIG. 17E).

In contrast, effector cells that were able to kill multiple tumor cellsand secrete IFN-γ exhibited a significantly higher out-of-contactmotility (d_(Well): 2.2±0.1 μm) compared to those that only secretedIFN-γ without killing (d_(Well): 1.6±0.1 μm), and the aforementionednon-functional T cells (p-value=0.043 and 0.002 respectively) (FIG.17E).

This observation of higher motility was also recorded with serial killereffector cells that did not secrete IFN-γ (d_(Well): 2.4±0.2 μm) incomparison with effector cells that only secreted IFN-γ ornon-functional cells (p-value=0.007 and 0.0002 respectively).Remarkably, these observations, however were not true for effector cellsthat were only capable of killing one tumor cell, as their averagedisplacement were not significantly higher compared to those that didnot kill, suggesting that serial killers perhaps benefit from the highmotility allowing for rapid discovery of targets within the localmicro-environment. These observations were only true for theout-of-contact motility and not surprisingly, regardless of the functionelicited, all functional effector cells showed no differences inmotility during conjugation with the tumor cell (FIG. 20).

Example 2.7. Transcriptional Profiling of Motile CAR⁺ T Cells Reveals anActivated Phenotype

Since the TIMING results indicated that the basal motility may be ableto identify polyfunctional killer cells, Applicants next sought todefine the underlying molecular profile of motile CD8⁺ T cells.Accordingly, a set of 90 genes relevant to T-cell function wereidentified, and multiplexed, single cell, RT-qPCR was performed (FIG.21). In order to study the basal motility of these CD8⁺ T cells, aTIMING experiment was set up to track individual live T cells withoutthe influence of the tumor cells. Single cells were picked up based ontheir motility profile: “motile or high motility” (d_(well): 2.6±0.8 μm,n=41) or “non-motile or low motility” (d_(well): 0.8±0.4 μm n=43) andtheir transcriptional profile determined (FIGS. 6A and 23). Aftermicrofluidic qPCR, and subsequent to filtering, t-test comparisons of 62genes between the motile and non-motile groups showed that 15 genes hadsignificantly altered level of expression (p<0.05) and more than a 1.5fold change: CD244, CD58, LAG3, CTLA4, CD86 (activation markers); CCR1,CXCR3, IL18R1, IL2RB, IL4R (chemokine and cytokine receptors), and GATA3(transcription factor) were upregulated, while CX3CR1, CCR4 (chemokinereceptors); CD69 (activation marker), and IRF4 (transcription factor)and were down-regulated (FIG. 22B). Unsupervised hierarchical clusteringwas performed with gene- and cell-normalized data of these 15 genes, andthe sample clustering achieved a classification according to the knowncategories (motile vs. non motile) with 83% accuracy (FIG. 22C).

When Applicants repeated the agglomerative clustering with themotility-specific features d_(well) and aspect ratio (AR, ratio ofminor/major axes) along the genes, the cluster tree structure waslargely unaltered and d_(well) was closely clustered with expression ofCD244 and IL2RB, while AR was highly correlated to IRF4 (FIG. 24). Whilethe comparisons of transcriptional profiles with Student's t-test andhierarchical clustering enabled Applicants to infer differences betweenthe motile and non-motile groups, Applicants hypothesized that theheterogeneity of this cell population could be also described as aprogression of cells characterized by gradual changes in gene expressionfrom cell to cell. The set of fifteen differentially expressed genes andthe two motility parameters, d_(Well) and AR, were used as the base setfor the subspace trend discovery tool STrenD that identified ten genesconsidered to support the progression (FIG. 22D). With the selectedgenes and features, STrenD outputs a tree structure representing theprogression of cells identified by the input features (FIG. 22E).

By visualizing and coloring the tree using TreeVis, Applicants canclearly identify non motile cells clustered together at the center-rightside of the tree, while motile cells split out of this pool into twobranches, one with high expression of IL2RB, IL18R1, CD58, LAG3 andGATA3 (FIG. 22E, upper left branch), one with low expression of theseand with very low expression of IRF4, but still with high motility andhigh CD244 expression (FIG. 22E, lower left branch).

Consistent with the observations outlined here, network analysis usingGeneMania confirmed that the major pathways associated with theidentified transcripts were related to positive T-cell activation andlymphocyte migration (FIG. 22F). Lastly, since one of the majormechanisms of immediate cytotoxicity mediated by CD8⁺ T cells is throughthe granzyme B/Perforin pathway, and since Applicants' TIMING resultsindicated that polyfunctional serial killer CD8⁺ T cells had a higherbasal motility, Applicants quantified the differences in expression ofthese specific transcripts within motile and non-motile cells. AlthoughGZMB was not significantly differentially expressed, PRF1 transcriptswere detected at significantly higher levels in motile cells(p-value=0.03, FIG. 25).

Example 2.8. Discussion

Applicants have demonstrated in this Example an integrated and modularhigh-throughput analytical pipeline for combined functional andmolecular profiling of T-cell behaviors. This single-cell assay providesan integrated method which not only tracks the key functional attributesof T cells including motility, cytotoxicity, and cytokine secretiondirectly, but also serves as a front-end screen for identifyingfunctional attributes that can be interrogated at the molecular levelusing multiplexed transcriptional profiling. Although Applicants havedemonstrated the application of this method in the context of T-cellbehaviors, the platform can be adapted to other cell types formonitoring combined cellular behaviors, protein secretion, andtranscriptional profiling.

The polyfunctionality of tumor-specific individual CD8⁺ CAR⁺ T cells,with regards to IFN-γ secretion and killing (and multi-killing) uponligation with tumor cells was evaluated. Among all functional T cells,the group that secreted IFN-γ as a monofunction displayed the longestduration of conjugation to the tumor cell, in comparison to the T cellsthat participated in lysis of target cells. Since all T cells wereuniformly modified with the CAR, and since the concentration of antigenon the target cells was uniform (FIG. 26), Applicants' results revealthat the duration of stable conjugation leading to different functionaloutcomes (IFN-γ vs. killing) can be heterogeneous. Applicants' resultsthus complement previous studies obtained by titrating antigenconcentration to show that CD8⁺ T cells can form a short lytic synapseat low antigen densities, and a long stable stimulatory synapse leadingto IFN-γ at high antigen densities. Significantly, Applicants' resultsat the single-cell level suggest that detachment from target cells mightbe enabled by killing, and the decision to terminate conjugation canoccur prior to Annexin V staining (FIG. 19).

In tracking the frequencies of serial killer T cells with and withoutsimultaneous IFN-γ secretion, no significant differences were observed,suggesting that the early termination of conjugation did not affectT-cell activation for IFN-γ secretion. Applicants' results demonstrateat the single-cell level that the duration of conjugation of T cells totarget cells might reflect different functional outcomes, in concordancewith a recent report combining population level functional studies andsingle-cell calcium activation on mouse/human T cells which showed thatfailed target detachment can lead to prolonged IFN-γ hyper-secretionfrom T cells and that initiation of caspase within target cells likelyenabled T cells to terminate the synapse.

In addition, tracking the displacement of CD8⁺CAR⁺ T cells revealed thatpolyfunctional cells and specifically serial killer T cells, exhibitedelevated out-of-contact basal motility in comparison to eithernon-functional T cells, or those effector cells that only secretedIFN-γ. In order to gain molecular insights into the immunological stateof highly motile cells, multiplexed transcriptional profiling wasperformed at the single-cell level, targeting genes associated withT-cell activation, differentiation and memory. Combined statisticaltesting using t tests and hierarchical clustering followed byprogression discovery modeling identified a core set of immunologicalgenes that may be useful in distinguishing motile and non-motile Tcells. Consistent with TIMING observations that motile T cells areenriched within the polyfunctional subset, molecular profiling indicatedthat markers associated with recent activation including CD244 (2B4),CD58, LAG3, IL2RB (CD122), IL18R1, the chemokine receptor CXCR3 and thetranscription factor GATA3 were upregulated within motile cells.Similarly, the transcripts for the pore forming protein, perforin,required for immediate cytotoxicity mediated by CD8⁺ T cells, were alsoupregulated within motile T cells (FIG. 25).

Individual T cells with increased motility also showed a matchedincrease in CXCR3 transcripts (FIG. 27A) which is one of the majorchemokine receptors associated with trafficking to the tumormicroenvironment and is expressed on activated TILs in diverse cancersincluding breast cancer and melanoma. The expression of CXCR3 isup-regulated upon CD8⁺ T-cell activation, and in addition to itsfunctional role in chemotaxis, CXCR3 derived signaling is believed toalso affect the development of both effector and memory CD8⁺ T cells.Similarly, the number of CD2 transcripts showed a positive correlationwith T-cell motility (FIG. 27B).

The dynamic molecular interaction between CD2 and its binding partnerCD58 facilitates T-cell recognition by stabilization of inter-cellcontacts. Since the single-cell transcriptional profiling indicated amatched up-regulation of CD58 and CD2 on motile T cells (FIG. 27C), itis possible that these molecules can mediate homotypic T-cell/T-cellinteractions and cluster formation, both of which are known to promoteT-cell activation, proliferation and differentiation in vitro and invivo. Of note, CD244 was also upregulated on motile T cells and is asimilar adhesion molecule that can regulate T-cell homotypicinteractions by binding to CD48 (FIG. 27D). Applicants thus propose anintegrated model that summarizes all of Applicants' results integratingmotility, serial killing, IFN-γ secretion and transcriptional profiling(FIG. 28).

In summary, Applicants' integrated methodology combining functional andmolecular screening enables investigation of complex cellular behaviorsat single-cell resolution. Applicants' modular and scalable method issuitable for screening combinations of the different T cell functionsthat might be required for the efficacy of T cells engineered with apanel of CARs and predicting whether an introduced immunoreceptor willresult in therapeutic success in vivo. The therapeutic potential of CAR⁺T cells for treatment of B-cell malignancies raises the question whethersimilarly-engineered T cells with alternative specificities will alsohave anti-tumor effects in humans. Thus, the study of geneticallymodified CD19-specific T cells serves as a foundation to advance ourunderstanding of CAR⁺ T cells that target other hematologic malignanciesand solid tumors.

Currently, most investigators rely on mouse experiments to inform onwhich CAR design and TIL population to advance to human application, butthis is not readily amenable to scale up. As demonstrated here,Applicants propose that high throughput in vitro systems can be employedto evaluate the functional characteristics of panels of T cells beforeselecting subsets for preclinical and clinical translation. Theimplementation of the microscopy tools revealed in this report and theobservation that motility correlates with killing of tumor cells mayprovide investigators with an approach to identify genetically modifiedT cells without the need for testing in small animals.

Example 2.9. Cell Lines, Primary T Cells, TILs, and Reagents

Human pre-B cell line NALM-6 (ATCC) and CAR⁺ T cells were cultured asdescribed previously. The cell lines were routinely tested to ensurethat they were free of mycoplasma contamination and flow-cytometry wasutilized to confirm the expression of CD19. TILs were isolated andexpanded as previously described. Briefly, initial TIL expansion wasperformed in 24-well plates from either small 3-5 mm² tumor fragments orfrom enzymatic digestion, followed by centrifugation with FICOLL. TILswere then allowed to propagate for 3-5 weeks in TIL-complete mediacontaining 6000 IU/mL human recombinant IL-2 (Prometheus). Once desirednumber of TIL was achieved, Rapid Expansion Protocol (REP) was performedin which TIL was cultured together with PBMC feeder cells (1 TIL: 200feeders) preloaded with anti-CD3 (OKT3, eBioscience) in a G-REX 100Mflask until the desired number of cells were achieved and harvested.

Example 2.10. Beads Preparation: Coating Beads with Primary CaptureAntibody

About 1 μL of Promag 3 Series goat anti-mouse IgG-Fc beads (˜2.3×10⁵beads) in solution was washed with 10 μL of PBS, and re-suspended in19.6 μL PBS (˜0.05% solids). Mouse anti-human IFN-γ (clone 1D1K) wasthen added to beads at final concentration of 10 μg/mL and incubated for30 min at room temperature (RT), followed by washing and re-suspensionin 100 μL PBS.

Example 2.11. ELISpot Assays

ELISpot assay was performed with fresh PBMC and TIL as previouslydescribed. Briefly, microwell plates were coated with capture antibodyanti-human IFNγ-1D1K at 10 μg/mL overnight at 4° C. The next day, theplates were washed twice in PBS and blocked with RPMI-PLGH+10% FBS for45 minutes at 37° C. Cells were prepared, as follows, in triplicates:(1) 4,000 PBMC stimulated with 10 ng/mL PMA/1 μg/mL ionomycin per well(2) 4,000 melanoma-specific TIL stimulated with 10 ng/mL PMA/1 μg/mLionomycin per well (3) 200,000 PBMC stimulated with 2 μg/mL CEF peptide(4) Corresponding non-stimulated cells. Next, cells were incubated for18 hours at 37° C./5% CO₂, followed by five washes with PBS and 2 hourincubation with biotinylated detection anti-human IFNγ 7-B61 at 37°C./5% CO₂ in PBS+0.5% FBS. After washing with PBS seven times, theimmunosandwich was completed with subsequent addition ofextravidin-alkaline phosphatase (1 hour incubation at 37° C./5% CO₂[Sigma-Aldrich]). The plate was washed five times with PBS, and BCIP/NBT(Sigma-Aldrich) substrate was added and incubated for 15 minutes at 37°C./5% CO₂. The plate was subsequently read with ELISpot reader (C.T.L.counter) while taking into account background measurement.

Example 2.12. Nanowell Array Fabrication and Cell Preparation

Nanowell array fabrication for interrogation of effector functions atsingle-cell level was performed as described previously. Approximately 1million effector cells and target cells were both spun down at 400×g for5 minutes followed by labeling with 1 μM PKH67 and PKH26 fluorescentdyes respectively according to manufacturer's protocol. Excess unbounddyes were then washed away and cells were re-suspended at ˜2 millioncells/mL concentration in complete cell-culture media (RPMI+10% FBS).

Example 2.13. Finite Element Simulations

The system of partial differential equations to model variation ofanalyte concentrations, C and C_(s), with time, was solved usingTransport of diluted species interface, Chemical reaction engineeringmodule in COMSOL Multiphysics 4.1. Mass balance equation involving Cswas solved using its weak form. Change in positions of cell and bead,convective transport, diffusion on the bead surface (D_(s)=10⁻²⁵ m²/s),non-specific adsorption on walls and degradation of analyte wereneglected to simplify numerical simulations.

Example 2.14. TIMING Assays for Multiplex Study of Effector CytolyticPhenotypes and IFN-γ Secretion

Capture antibody coated beads and labeled effector and target cells wereloaded consecutively onto nanowell arrays. Whenever necessary, arrayswere washed with 500 μL of cell culture media to remove excess beads orcells. Next, detection solution containing Annexin V-Alexa Fluor 647(AF647) (Life Technologies) (for detection of target apoptosis) wereprepared by adding 50 μL solutions from stock to 2.5 mL of completecell-culture media without phenol red. Nanowell arrays were then imagedfor 5 hours at intervals of 5 minutes using LEICA/ZEN fluorescentmicroscope utilizing a 20×0.45 NA objectives and a scientific CMOScamera (Orca Flash 4.0). Subsequently, mouse anti-human IFN-γ biotin wasadded to 2.5 mL cell media above at 1:1000 dilution. This was incubatedfor 30 minutes followed by washing and incubation with 5 μg/mLStreptavidin-R-Phycoerythrin (PE). The entire chip was again imaged todetermine the intensity of PE signal on the microbeads and the twodatasets were matched using custom informatics algorithms.

Example 2.15. Image Processing, Cell Segmentation and Tracking, and DataAnalytics

Image analysis and cell segmentation/tracking were performed asdescribed previously. The pipeline of image processing and cellsegmentation ends with statistical data analysis based on the tabularspatio-temporal measurement data generated by the automated segmentationand cell tracking algorithms. Nanowells containing 1 effector and 2-5tumor cells were selected for further analysis. Next, Applicantspartitioned all these events based on the functionalities of the cells(i.e. mono-kill, serial kill, and IFNγ secretions). A size-exclusionfilter based on maximum pixel areas were used to effectivelydifferentiate cells from beads (i.e., beads were much smaller thancells). Where specified, cell tracks were represented using MATLAB(Mathworks Inc. MA).

Example 2.16. Gene Expression Profiling

PKH green stained CD8⁺ T cells were loaded on a nanowell array, immersedwith Annexin-AF647 (Life Technologies) containing phenol red freecomplete cell-culture medium and imaged for 3 hours using TIMING exactlyas described above. After carefully washing the cells on the chip 3times with cold PBS (4° C.), cells were kept at 4° C. until retrieval.Time-lapse sequences were manually analyzed to identify live high andlow motility cells. The cells were individually collected using anautomated micro-manipulating system (CellCelector, ALS) and deposited innuclease free microtubes containing 5 μL of 2× CellsDirect buffer andRNAse Inhibitor (Invitrogen). Single cell RT-qPCR was then performedusing the protocol ADP41 developed by Fluidigm. Ninety-two cells (48motile and 44 non motile) were assayed, along with bulk samples of 10and 100 cells, and with no-cell and no-RT controls. The panel of 95genes (FIG. 21) included genes relevant to T cell activation, signalingand gene regulation, and was designed and manufactured by Fluidigm D3AssayDesign.

For data analysis, Applicants first extracted Log 2Ex value bysubtracting Ct values from a threshold of 29, as described previously.Applicants then excluded data from i) cells that had less than 40% ofgenes that were amplified and had a mean of Log 2Ex out of the range ofpopulation mean±3SD and from ii) genes that were amplified in <10% ofcells. Post-process analysis was done using Excel (Microsoft), Prism(GraphPad), MeV, STrenD(https://github.com/YanXuHappygela/STrenD-release-1.0) and Genemaniawebtool (http://www.genemania.org/).

Example 3. Individual Motile CD4⁺ T Cells can Participate in EfficientMulti-Killing Through Conjugation to Multiple Tumor Cells

In this Example, Applicants implemented TIMING to provide directevidence that CD4⁺CAR⁺ T cells (CAR4 cells) can engage in multi-killingvia simultaneous conjugation to multiple tumor cells. Comparisons of theCAR4 cells and CD8⁺CAR+ T cells (CAR8 cells) demonstrate that while CAR4cells can participate in killing and multi-killing, they do so at slowerrates, likely due to the lower Granzyme B content. Significantly, inboth sets of T cells, a minor sub-population of individual T cellsidentified by their high motility, demonstrated efficient killing ofsingle tumor cells. By comparing both the multi-killer and single killerCAR⁺ T cells, it appears that the propensity and kinetics of T-cellapoptosis was modulated by the number of functional conjugations. Tcells underwent rapid apoptosis. Moreover, at higher frequencies (i.e.,when T cells were conjugated to single tumor cells in isolation), thiseffect was more pronounced on CAR8 cells.

Applicants' results suggest that the ability of CAR⁺ T cells toparticipate in multi-killing should be evaluated in the context of theirability to resist activation induced cell death (AICD). Applicantsanticipate that TIMING may be utilized to rapidly determine the potencyof T-cell populations and may facilitate the design and manufacture ofnext-generation CAR⁺ T cells with improved efficacy.

Example 3.1. Cell Lines and Antibodies

All antibodies were purchased from Biolegend (San Diego, Calif.). Humanpre-B cell line NALM-6 (ATCC), Daudi-β2m (ATCC), T-cell lymphoma EL-4(ATCC) and modified CD19⁺EL-4 cells were cultured as describedpreviously. The cell lines were routinely tested to ensure that theywere free of mycoplasma contamination and flow-cytometry was utilized toconfirm the expression of CD19.

Example 3.2. Genetic Modification and Propagation of Cells

PBMC from healthy volunteers were electroporated using Nucleofector II(Amaxa/Lonza) with DNA plasmids encoding for second generation CAR(designated CD19RCD28) and SB11 transposase and co-cultured withγ-irradiated K562 aAPC (clone 4) for 28 days along with cytokines (IL-2and IL-21) in a 7-day stimulation cycle as described previously. Forsingle cell analysis, frozen CAR⁺ T cells were revived and re-stimulatedwith irradiated K562 aAPC before using them in experiments.

Example 3.3. Flow Cytometry

Cells were stained for cell surface markers (CAR, CD4, CD8, CD3), fixedand permeabilized (Cytofix/Cytoperm, BD Biosciences) for 20 minutes at4° C. Cells were subsequently stained for intracellular granzyme B inperm/wash buffer at 4° C. for 30 minutes, acquired on a FACS Calibur,and analyzed using FCS Express/FlowJo as previously describedStatistical analyses for determining GzB expression were performedwithin R.

Example 3.4. End-Point Cytotoxicity Assay

Nanowell array fabrication and the corresponding cytotoxicity assay tointerrogate effector-target interaction at single-cell level wereperformed as described previously. Briefly, CAR⁺ T cells labeled with 1μM of red fluorescent dye, PKH26 (Sigma) and target cells labeled with 1μM of green fluorescent dye PKH67 were co-loaded onto nanowell arrays ata concentration of 10⁶ cells/mL. Images were acquired on a Carl ZeissAxio Observer fitted with a Hamamatsu EM-CCD camera using a 10×0.3 NAobjective. Automated image acquisition of the entire chip was performedat 0 and 6 hour and apoptosis was identified by staining with AnnexinVconjugated to Alexa-647 (Life Technologies, Carlsbad, Calif.).

Example 3.5. TIMING Assays

Nanowell grids were fixed in position on a 60 mm petridish. The cellswere labeled and loaded exactly as described for the end-point assay andimaged on a Zeiss Axio Observer using a 20×0.45 NA objective. Imageswere acquired for 12-16 hours at intervals of 7-10 minutes.

Example 3.6. Flow Cytometry Based Cytotoxicity Assays

CAR4 cells (1×10⁶ cells) were incubated with CD19⁺ target cells (0.2×10⁶cells; Daudiβ₂m, NALM-6, CD19EL-4) at E:T ratio of 5:1 in the presenceor absence of 5 mM EGTA in 24-well plates in 5% CO₂ at 37° C. for 6hours. Following incubation cells were stained for CD3 (T cells) andCD19 (tumor targets), acquired on a FACS Calibur (BD Biosciences) andanalyzed using FCS Express version 3.00.007 (Thornhill, Canada).

Example 3.7. Image Processing and Cell Segmentation

In order to permit accurate computation of cell displacements despitecamera and stage movements, the individual nanowells were detectedautomatically with >99% accuracy by correlating pre-constructed shapetemplates at the expected range of orientations and magnificationvalues. The correlation value is a maximum at the well centers, andthese points are detected using a local maxima clustering algorithm. Thecells in each image channel are analyzed automatically using a 3-stepmethod. First, each pixel is stratified as bright foreground,intermediate foreground, and dark background based on modeling imageintensities as a mixture of three Gaussian distributions. The foregroundpixels are subjected to multi-level thresholding (Applicants used 10equally-spaced levels between the maximum and minimum foregroundintensity values). The cell centers are detected using a local maximaclustering on the average of Euclidean distance maps computed at eachthreshold. Using these cell centers, the image foreground is partitionedinto individual cell regions using the normalized cuts algorithm,allowing cell sizes and shapes to be quantified. Spectral overlapbetween the dyes used under the imaging conditions were eliminatedduring image processing through an automatic “unmixing” process, andthis is performed independently for each set of experiments. Inaddition, the segmentation scripts calculate an integrated fluorescenceintensity by averaging on all the pixels associated with a given celland thus eliminated any ambiguity in effector/target classification dueto the diffusion of dyes across the cell membrane during contact.

Example 3.8. Cell Tracking

The detected cells, denoted C=_(i=1 . . . N) ^(t=1 . . . T), where N isthe number of cells in the well and T is the number of frames, aretracked from frame to frame using a graph-theoretic edge selectionalgorithm on a directed graph where cells correspond to vertices andedges represent temporal association hypotheses. The association costfor each edge f_(i,j) ^(t) between object i at time t and object j attime t+1 is calculated based on cell location and size. The temporalcorrespondences are identified using an integer programming algorithmthat maximizes the total association cost subject to constraints toensure that each cell in a given frame is associated with a maximum ofone cell in the subsequent frame, and vice versa.

Example 3.9. Production and Phenotype of CAR⁺ T Cells

Genetically modified and propagated T cells were generated from theperipheral blood mononuclear cells (PBMC) of healthy volunteer donorsderived using the Sleeping Beauty (SB) system²⁷ to enforce expression ofa second generation CD19-specific CAR (designated CD19RCD28) thatactivates T cells via a chimeric CD3 and CD28 endodomain (FIG. 29A).Subsequent to expansion, CAR⁺ T cells from two separate donors containedpredominantly CD8⁺ T cells (FIG. 29B).

Example 3.10. The Cytotoxic Potential, Specificity and Multi-KillingAbility of Individual CAR⁺ T Cells

Donor-derived CAR⁺ T-cell populations were evaluated for their abilityto lyse CD19⁺EL4 target cells, by co-culture within nanowell grids(FIGS. 29C and 30). At an E:T of 1:1, averaged across both donors, 29%of single CAR⁺ T cells induced apoptosis of (number of events,N_(total)=4,048) CD19⁺EL4 cells within six hours, whereas they inducedapoptosis of just 1% (N_(total)=3,682) of CD19⁻EL4 cells in the sametime frame. The >29-fold increase of lysis of CD19⁺ versus CD19⁻ targetsconfirms TAA-specific lysis (FIG. 29D, p-value<0.0001, Fisher's 2×2test). In parallel, a conventional 4-hour ⁵¹Chromium release assay (CRA)was performed at the same E:T ratio (1:1) and reported a similar overallmagnitude of target cells killing (mean 14-fold increase of lysis ofCD19⁺ versus CD19⁻EL4 cells), albeit without single-cell resolution(FIG. 29D). The ability to redirect specificity to lyse human CD19⁺tumor cells was confirmed using the pre-B cell line NALM-6 (data notshown).

When averaged across both donors, within six hours of observation,individual CAR⁺ T cells induced apoptosis in 34% (N_(total)=3,503) ofNALM-6 target cells at an E:T ratio of 1:1. Across all of the samplestested, single cell assay demonstrated a linear correlation to the CRA(FIG. 29D, r²=0.84, p-value=0.01). The ability of individual T cells toeliminate more than one target cell was quantified by analyzingnanowells containing multiple targets (FIG. 31). Averaged across bothdonors, at an E:T ratio of 1:2, within six hours, 21% (N_(total)=2,294)of single CAR⁺ T cells killed exactly one CD19⁺EL4 target-cell whereas23% killed both targets (FIG. 29E).

During this same timeframe, at an E:T ratio of 1:3, 22%(N_(total)=1,108) of single CAR⁺ T cells killed exactly one target, 22%killed exactly two targets, and 9% killed all three targets (FIG. 29E).Thus, within a defined observation window, the likelihood that anindividual CAR⁺ T cell killed more than one tumor cell improved as thenumber of targets within the nanowell increased, even though this mightreflect higher frequency of interactions at higher cell densities.

The aforementioned findings were also observed when substituting NALM-6as target cells, albeit with diminished frequency of multi-killing after6 hours of co-culture (FIG. 32). In aggregate, these data demonstratethat the responses measured by the single-cell assay are consistent withthe results of CRA, and that multi-killer CAR⁺ T cells (ability to lyseat least two targets) comprised 20% (N_(total)=3,402) of the CAR⁺ T-cellpopulation.

Example 3.11. Motile CD8⁺ Cytotoxic T Cells are Efficient Killers withDecreased Potential for Activation Induced Cell Death (AICD)

In order to gain an improved mechanistic understanding on theinteraction between individual CAR⁺ T cells and NALM-6 tumor cells,Applicants implemented the TIMING assay illustrated in FIG. 33. Sixparameters describing T-cell intrinsic behavior motility (d_(Well)) andaspect ratio of polarization (AR), conjugation (contact lasting >7minutes, t_(Seek) and t_(Contact)), and death (t_(Death) and t_(AICD))were computed to define each interacting pair of effector and tumorcells (FIGS. 34A-C). At an E:T of 1:1, 77% (N_(total)=268) of singleCD8⁺CAR⁺ T cells (CAR8 cells) that made at least one conjugate were ableto kill the engaged leukemia cell. In order to identify subgroups of Tcells that exhibited different behavioral interactions with the tumorcells leading to subsequent killing, the time series data for each ofthree features, total duration of conjugation, d_(well) and AR,underwent hierarchical clustering (FIG. 35).

Three T-cell subgroups were described that collectively accounted for70% of the single-killer CAR8 cells: S1 (14% [7-20%], range), lowconjugation and high motility; S2 (49% [32-66%]), high conjugation andlow motility; and S3 (21% [19-22%]), low conjugation and low motility(FIG. 35). The high-motility subgroup, S1, comprised predominantly ofelongated T cells that had an initial “lag-phase” (t_(Seek) 184±38minutes, Mean±SEM), but formed stable conjugates (t_(Contact) 98±13minutes) prior to target apoptosis (t_(Death) 204±35 minutes) (FIGS.34D-F). Predominantly, these T cells exhibited a decrease in motilityand increased circularization during tumor-cell conjugation, detachedafter tumor-cell death, resumed normal migratory function and had only alow frequency of effector cells undergoing AICD (data now shown).

The representative cell in the dominant subgroup, S2, establishedconjugation quickly (t_(Seek) 36±6 minutes), and displayed sustainedconjugation (t_(Contact) 145±16 minutes) prior to killing (t_(Death)158±18 minutes) (FIGS. 34E-F). The majority of these T cells did notdetach or resume migratory function after tumor-cell lysis, retained apredominantly circular morphology, and continued to remain conjugatedfor more than 10 hours, even subsequent to the death of the conjugatedtumor-cell. Moreover, 88% of S2 effector cells underwent apoptosiswithin the first ten hours of observation (data not shown). Finally, Tcells in the S3 subgroup were rapid killers (t_(Contact) 84±8 minutesand t_(Death) 118±20 minutes) that arrested after conjugation but failedto resume migration after tumor-cell detachment/killing (FIGS. 34E-F).Although these S3 effectors detached from tumor-cells after deliveringthe lethal hit, 53% then underwent apoptosis (data not shown).

Taken together these results demonstrate that at an E:T ratio of 1:1,the dominant subgroup of cells, S2, identified by their lack of motilityand early conjugation to tumor cell, underwent AICD. On the contrary,highly motile CAR8 cells, S 1, detached efficiently and resumedexploration of the local microenvironment, indicating that the motilityof CAR8 cells might help identify efficient killers with decreasedpropensity for AICD. The observation that the majority of the CAR8 cells(S2 subgroup) maintained extended contact even after the death of thetumor cell is consistent with investigations on HIV-specific CTLs.

Example 3.12. CAR8 Cell Motility at Increased Tumor-Cell DensitiesFacilitates Multiplexed Killing

The efficacy of CAR⁺ T cells to eliminate tumor burden in excess of thenumber of effectors infused is due to their ability to persist andparticipate in serial killing. To facilitate identification ofmulti-killers, Applicants next profiled the interactions in nanowellscontaining a single CAR8 cell and 2 to 5 NALM-6 tumor cells (E:T 1:2-5).The frequency of CAR8 cells that were able to simultaneously conjugateto two or more tumor cells increased from 25% to 49% as the number oftargets within the nanowell increased, indicating that multiplexedkilling might be important (FIG. 36A). The frequency of simultaneoustumor conjugates that result in tumor cell deaths (46% [43-50%]) was notvery different from true serial killers that attach, kill, detach andattach to a different tumor cell (49% [44-53%]), suggesting that CAR8cells are capable of eliciting either mode of killing, likely dependenton tumor cell density. Individual multi-killer CAR8 cells (N_(total)=70)demonstrated only a small decrease in motility when conjugated to onetumor cell but showed no significant change in motility upon conjugationto multiple tumor cells (d_(Well)(unconjugated): 5.9±0.5 μm vs d_(Well)(single target): 4.6±0.3 μm vs d_(Well) (two targets): 4.7±0.3 μm) (FIG.36B).

The only difference for multi-killers when contacting the differenttumor cells was in their time to establish conjugates (t_(Seek) Target₁:18±4 minutes vs Target₂: 98±13 minutes, FIG. 36C). Both, duration ofconjugation (t_(Contact) Target₁: 101±9 minutes vs Target₂: 113±15minutes) and killing efficiency (t_(Death) Target₁: 156±17 minutes vsTarget₂: 177±24 minutes) were no different (FIG. 36D).

In addition to contact duration, the number of CAR8 cell tumor cellconjugations that lead to killing during encounter with the first tumorcells (61% both donors) was also not significantly different from thenumber of conjugations that resulted in target cell killing duringencounter with the second tumor cell (74% [70-79%]). These TIMING datasuggest that the efficiency to kill a second tumor cell is largelyunaffected by the hit on a first target (p-value>0.99). Furthermore, incomparison to single killer CAR8 cells, multi-killer CAR8 cellsdisplayed greater motility when conjugated to the tumor cell despite theincreased crowding because of higher tumor cell density.

Example 3.13. Motility can Identify a Subgroup of CAR4 Cells withEnhanced Cytotoxic Efficiency

Next, the interaction of individual CAR4 cells from two donor-derivedpopulations (FIG. 37A), with NALM-6 tumor cells were profiled usingTIMING. At an E:T ratio of 1:1, 55% (N_(total)=549) of single CAR4 cellsthat conjugated to a NALM-6 cell subsequently killed the tumor cell. Aswith the CAR8 cells, the interaction behavior of CAR4 cells with theNALM-6 cells could be classified into three subgroups, S1-S3 (FIG. 38).CAR4 cells in the enhanced motility subgroup, S1 (11% both donors),displayed significantly faster kinetics of tumor cell death (t_(Death)157±17 minutes) compared to the dominant S2 (34% [31-36%]) subgroup(t_(Death) 318±23 minutes, FIGS. 16B-D). This increased kineticefficiency was consistent with the decreased conjugation time requiredby the S1 subgroup of cells (t_(Contact) 122±11 minutes) in comparisonto the S2 subgroup (t_(Contact) 300±21 minutes) (data not shown). Theseresults suggest that similar to CAR8 cells, the motility of the CAR4cells may help identify the most efficient killers.

Example 3.14. Both Single-Killer and Multi-Killer CAR4 Cells RequiredLonger Conjugation and Demonstrated Delayed Kinetics of Killing inComparison to CAR8 Cells

At the E:T ratio of 1:1, comparisons of the killing efficiency of CAR4cells (t_(Death) 284±11 minutes) and CAR8 cells (163±12 minutes)demonstrated that individual CAR4 cells on average required two extrahours to induce tumor cell death (FIG. 37E). Consistent with theobservation that the S2 subgroup is the dominant population of CAR⁺ Tcells, CAR4 cells in the S2 subgroup (t_(Death) 318±23 minutes)demonstrated delayed kinetics of killing in comparison to CAR8 cellswithin the S2 subgroup (t_(Death) 158±18 minutes) (data not shown). Asmentioned herein, since the motility of CAR4 cells could be used toidentify the most efficient killers (FIG. 37C), comparisons of thekinetic efficiency of CAR4 cells in the S1 subgroup (t_(Death) 157±17minutes) with CAR8 cells in the S1 subgroup (t_(Death) 204±34 minutes)demonstrated no significant differences. This further supports thenotion that motility might be a useful parameter in identifyingefficient cytolytic CAR⁺ T cells.

Comparisons of the single-cell behavioral interactions of multi-killerCAR4 cells (N_(total)=78) with the CAR8 cells demonstrated that mostfeatures were conserved across cells of both phenotypes. First, theunconjugated motility of CAR4 cells (d_(well) 6.9±0.5 μm) was nodifferent than CAR8 cells (d_(well) 5.9±0.5 μm, FIG. 39A). Second, likeCAR8 cells, CAR4 cells demonstrated a matched decrease in motility (FIG.39A) and increased circularization when conjugated to one or more tumorcells. Third, the preferred contact mode of the multi-killer CAR4 cellswas also simultaneous conjugations to multiple tumor cells (data notshown). Fourth, simultaneous conjugates that result in killing accountedfor 61% [60-63%] of multi killing events, indicating that this is animportant mode of killing intrinsic to T cells and not just CD8⁺ Tcells. Fifth, comparisons of t_(Death) for the different tumor cellskilled by individual multi-killer CAR4 cells demonstrated no differences(FIG. 39B). Lastly, the number of CAR4 cell tumor cell conjugations thatlead to killing during the first tumor cell encounter (60% [58-61%]) isnot significantly different from the number of contacts that leads tokilling when encountering the second tumor cell (60% [57-63%]),suggesting that the killing efficiency is unchanged.

Consistent with the observations at an E:T of 1:1, multi-killer CAR4cells required extended conjugation (t_(Contact) 214±18 minutes) anddemonstrated slower kinetics prior to killing the first tumor cell(t_(Death) 310±23 minutes) in comparison to CAR8 cells (FIG. 39B). Inaggregate, these results demonstrate that the major difference in CAR4cells and CAR8 cells participating in either single killing ormulti-killing is the kinetics of tumor cell death.

Example 3.15. Intracellular GzB Content can Explain Differences inKilling Efficiency

To test the hypothesis that the varying efficiencies both between cellsof the same population and in comparing CAR4 cells with CAR8 cells mightbe due to differences in expression of cytotoxic enzymes, Applicantsemployed intracellular staining at the single-cell level using flowcytometry to identify the expression GzB within these cells. Toestablish baseline controls, the intracellular GzB content of CD3⁺CD4⁺cells (2.36±0.01) and CD3⁺CD8⁺ cells (3.89±0.04) in PBMC of two separatedonors was determined (FIG. 39C). Consistent with our previous reports,both CAR4 cells (38.6±0.2) and CAR8 cells (267±2) showed significantlyincreased expression of GzB, in comparison to the controls (FIG. 39C).In agreement with the killing efficiency data (FIG. 39B), CAR4 cellsexpressed lower amounts of GzB in comparison to CAR8 cells, suggestingthat the origin of the differing kinetic efficiencies of these cellsmight be the differences in GzB content (FIG. 39C).

In order to quantify the contribution GzB secretion to tumor cellkilling at the single cell level, the ability of CAR4 cells to killtumor cells in the presence of the calcium chelator EGTA was studiedusing flow cytometry. EGTA blocks cytotoxic granule exocytosis, andhence should eliminate GzB mediated killing. Not surprisingly, CAR4cells co-cultured with tumor cells in the presence of 5 mM EGTA,demonstrated a substantial reduction in tumor cell killing across threedifferent cell lines, Daudi-β2m, NALM-6 and CD19⁺EL4 (FIG. 39D). Themost striking reduction was seen with Daubi-β2m tumor cells, whereinCAR4 cell mediated killing was completely abolished (FIG. 39D).

Example 3.16. CAR⁺ T-Cell Fate is Dependent on Tumor-Cell Density

AICD is a mechanism by which T cells undergo programmed apoptosis inresponse to functional activation. The frequency and kinetics ofindividual cytolytic CAR⁺ T cells to undergo AICD was monitored underthe two conditions: at high and low tumor densities. CAR8 cells inducingapoptosis of single targets demonstrated significantly faster kineticsof AICD (t_(AICD) 221±14 minutes) in comparison to the multi-killer CAR8cells from the same donors (t_(AICD) 371±29 minutes, FIG. 40A). Thistrend of faster AICD kinetics at lower tumor cell density was alsoobserved with CAR4 cells, although with delayed kinetics (FIG. 40A).Direct comparisons of the cells of different phenotypes at the sametumor cell density indicated that single-killer CAR8 cells underwentfaster AICD (t_(AICD), 221±14 minutes) in comparison to CAR4 cells(t_(AICD) 328±19 minutes) (FIG. 40A). Consistent with the expectationthat multi-killers efficiently resist AICD, these T cells from three offour donors displayed low frequencies of cells undergoing AICD (13-25%,FIG. 40B). However, multi-killer T cells from the last donor displayedAICD at elevated frequencies (58%) underscoring that the efficiency ofmulti-killers to execute multiple tumor cells must be evaluated in thecontext of their ability to resist AICD (FIG. 40B).

Applicants confirmed that the effector apoptosis that was observedrequired functional antigenic stimulation by co-incubating CAR8 cellswith CD19⁻EL4 cells within nanowell grids and imaged them using TIMING.The frequency of apoptotic effectors under these conditions was only 4%and this also confirmed that phototoxicity was negligible under thecurrent imaging conditions.

Significantly, across all four donors, the frequencies of cytolytic CAR⁺T cells undergoing AICD was higher at an E:T of 1:1 in comparison to themulti-killer CAR⁺ T cells, and this effect was more exaggerated withCAR8 cells (FIG. 40B). These data may help account for the decrease innumber and even disappearance of infused CAR⁺ T cells when the CD19⁺tumor mass is reduced.

Example 3.17. Summary

In this Example, Applicants implemented a high-throughput single-cellassay (TIMING) to dynamically profile the functionality of CAR⁺ T cells.Applicants' analyses at the single-cell level demonstrate that much likeCAR8 cells, CAR4 cells can directly engage in tumor cell killing, albeitwith altered kinetics. Applicants further demonstrate that CAR4 cellscan participate in multi-killing via simultaneous conjugation tomultiple tumor cells.

At low tumor cell densities (E:T 1:1), the majority of the single killerCAR8 cells were significantly faster in killing tumor cells incomparison to individual CAR4 cells (FIG. 37E). By contrast, both singlekiller CAR8 and CAR4 cells within the 51 subgroup, characterized bytheir high basal motility, displayed no significant differences in thekinetics of tumor cell killing. Furthermore, in contrast to the rest ofthe population, effector apoptosis was infrequent amongst CAR8 and CAR4cells in the 51 subgroup. Collectively, these data suggested that thehigh basal motility of CAR⁺ T cells (CAR4 or CAR8) might help identifyefficient killers with decreased propensity for AICD.

When interacting with increased numbers of tumor cells (E:T ratios of1:2 to 1:5), both individual CAR4 and CAR8 cells efficiently conjugatedto multiple tumor cells, facilitating multiplexed killing. Comparisonsamongst the different tumor cells killed by these individualmulti-killer CAR4/CAR8 cells demonstrated that they displayed anessentially unchanged efficiency (t_(Contact)) of killing of not onlythe first and second target killed, but also in comparison to(single-killer) CAR⁺ T cells that were incubated with only one tumorcell (data not shown). In comparing CAR4 cells with CAR8 cells however,consistent with the observations at an E:T ratio of 1:1, CAR4 cells weresignificantly slower in tumor cell killing. Intracellular staining atthe single-cell level indicated that the molecular origin of thedifferences in kinetic efficiency of the CAR4 and CAR8 cells could beattributed to their GzB content and this was further confirmed byblocking granule exocytosis using EGTA (FIG. 39).

For both CAR4 and CAR8 cells, single killer effectors underwentapoptosis at higher frequencies and with faster kinetics in comparisonto multi-killer CAR⁺ T cells (FIGS. 29 and 37). These data indicate thatactivation for lysis through multiple targets as opposed to prolongedconjugation with a single target reduces the propensity for effectorapoptosis. Although the mechanistic basis for the responsiveness ofthese T cells to antigen/target density is not known, it is conceivablethat the continuous propagation of these cells on irradiated aAPC atdefined ratios, allows for balanced activation while minimizing AICD.Collectively, these data could provide mechanistic insights intoobservations that infused CAR⁺ T cells swell in number in response toaddressing large numbers of CD19⁺ tumor cells, but then decline innumber as the tumor bioburden is lowered due to the multi-killing byeffector T cells.

In aggregate, comparisons of the CAR4 cells and CAR8 cells demonstratethat while CAR4 cells can participate in killing and multi-killing, theydo so at slower rates, likely due to the lower GzB content. Thisdecreased kinetic efficiency however is likely a minor disadvantage andis counter balanced by their decreased propensity of these cells toundergo AICD in the absence of help from other cells, as profiled inApplicants' nanowell system. Although Applicants have focused on theheterogeneity amongst CAR⁺ T cells in this Example, the resultspresented here are also likely influenced by the underlyingheterogeneity in tumor cells. While the expression of CD19 is uniform onthe cells used as targets in Applicants' assays, it is feasible thatthere could be subpopulations of tumor cells that are resistant to CAR⁺T-cell mediated killing.

Example 4. Single-Cell Metrics of the Efficacy of CAR+T Cells

CD19-specific CAR⁺ T cells for the treatment of B-cell malignanciesinclude a heterogeneous population. Among the most well describedfunctional attributes of T-cell anti-tumor efficacy are cytotoxicity(against tumor cells) and ability to persist. Direct measurement ofthese T cell functions at the single-cell level requires thesimultaneous monitoring of multiple parameters, including cell-cellinteractions, cell migration, gene expression, their ability to killtarget cells and the survival of the effector cells.

In this Example, Applicants demonstrate that single-cell methodologiescan be used to characterize CAR⁺ T cell potency a priori in vitro. In acomparison of two different CAR constructs, Applicants showed that invitro potency defined as cytotoxicity against tumor cells was consistentwith in vivo efficacy to control tumor cell progression. Further, theapproach allowed Applicants to identify efficient killer CAR⁺ T cells asexpressing higher levels of granzyme B, (GZMB), CD137 (41BB) and TIM3(HAVCR2).

As illustrated in FIG. 41, a TIMING assay followed by single cell geneexpression profiling was utilized. As shown in FIG. 42, comparison ofCAR constructs in vitro demonstrated optimal motility of T cellsexpressing CD8 hinge over IgG4 hinge. As shown in FIG. 43, CD8 hingeCAR⁺ T cells kill tumor cells faster and in higher numbers.

Single CAR⁺ T cells were retrieved after a 4 hour TIMING assay andassayed by multiplexed RT-qPCR. The gene expression profile of cytotoxicCAR⁺ T cells reveals higher expression levels of CD137, TIM3 and GZMBtranscripts. Volcano plot of genes transcripts (cytotoxic vs noncytotoxic) are shown in FIG. 44. A Venn diagram of differentiallyexpressed genes for 3 donors is shown in FIG. 45.

FIG. 46 shows that CD137 is expressed at higher levels in activated CAR⁺T cells and in degranulating CAR⁺ T cells. FIG. 47 shows that CD137stimulation decreases exhaustion markers while TIM3 targeting inducesCTLA4. FIG. 48 shows that targeting CD137 and TIM3 increasescytotoxicity of CAR⁺ T cells. FIG. 49 shows that targeting CD137 andTIM3 increases CAR⁺ T cells kinetics of killing and serial killing.

In sum, this Example demonstrates that TIMING provides dynamicmonitoring of individual T cells in vitro and allows for simultaneousmeasurement of cytotoxicity, cytokine secretion and gene expression atsingle cell resolution. Moreover, in vitro observations of the motilityand functionality of individual CAR⁺ T cells can predict efficacy invivo.

Applicants also demonstrate in this Example that CD137 is dynamicallyinduced on cytotoxic CAR⁺ T cells. Moreover, subsequent targetingimproves cytotoxicity of CAR⁺ T cells while decreasing exhaustion. TIM3transcripts are enriched in cytotoxic cells and targeting at the proteinlevel boosts cytotoxicity of CAR⁺ T cells.

Without further elaboration, it is believed that one skilled in the artcan, using the description herein, utilize the present disclosure to itsfullest extent. The embodiments described herein are to be construed asillustrative and not as constraining the remainder of the disclosure inany way whatsoever. While the embodiments have been shown and described,many variations and modifications thereof can be made by one skilled inthe art without departing from the spirit and teachings of theinvention. Accordingly, the scope of protection is not limited by thedescription set out above, but is only limited by the claims, includingall equivalents of the subject matter of the claims. The disclosures ofall patents, patent applications and publications cited herein arehereby incorporated herein by reference, to the extent that they provideprocedural or other details consistent with and supplementary to thoseset forth herein.

What is claimed is:
 1. A method of evaluating cellular activity, saidmethod comprising: (a) placing a cell population on an area; (b)assaying for a dynamic behavior of the cell population as a function oftime; (c) identifying one or more cells of interest based on the dynamicbehavior; (d) characterizing a molecular profile of the one or moreidentified cells; and (e) correlating the information obtained fromsteps (b) and (d).
 2. The method of claim 1, further comprising a stepof obtaining the cell population.
 3. The method of claim 2, wherein thecell population is obtained from a tissue or a blood sample.
 4. Themethod of claim 1, wherein the cell population is obtained by a methodselected from the group consisting of flow cytometry, positive flowsorting, negative flow sorting, magnetic sorting, and combinationsthereof.
 5. The method of claim 1, wherein the cell population comprisescells selected from the group consisting of plant cells, fungi cells,bacterial cells, prokaryotic cells, eukaryotic cells, unicellular cells,multi-cellular cells, immune cells, and combinations thereof.
 6. Themethod of claim 1, wherein the cell population comprises immune cells.7. The method of claim 1, wherein the cell population comprises cellsselected from the group consisting of T cells, B cells, monocytes,macrophages, neutrophils, dendritic cells, natural killer cells,fibroblasts, stromal cells, stem cells, progenitor cells, tumor cells,tumor stem cells, tumor infiltrating lymphocytes, and combinationsthereof.
 8. The method of claim 1, wherein the cell population comprisesT cells.
 9. The method of claim 8, wherein the T cells are selected fromthe group consisting of helper T cells, cytotoxic T cells, naturalkiller T cells, genetically modified T cells, chimeric antigen receptor(CAR) modified T cells, and combinations thereof.
 10. The method ofclaim 1, wherein the cell population comprises tumor cells and immunecells.
 11. The method of claim 1, wherein the cell population is placedon the area as individual cells.
 12. The method of claim 1, wherein thearea comprises a plurality of containers.
 13. The method of claim 12,wherein the containers are in the form of at least one of wells,channels, compartments, and combinations thereof.
 14. The method ofclaim 12, wherein the containers are in the form of an array.
 15. Themethod of claim 1, wherein the dynamic behavior is selected from thegroup consisting of cellular activation, cellular inhibition, cellularinteraction, protein expression, protein secretion, metabolitesecretion, changes in lipid profiles, microvesicle secretion, exosomesecretion, microparticle secretion, changes in cellular mass, cellularproliferation, changes in cellular morphology, motility, cell death,cell cytotoxicity, cell lysis, cell membrane polarization, establishmentof a synapse, dynamic trafficking of proteins, granule polarization,calcium activation, metabolic changes, small molecule secretion, protonsecretion, and combinations thereof.
 16. The method of claim 1, whereinthe dynamic behavior comprises motility.
 17. The method of claim 1,wherein the dynamic behavior comprises cellular interaction.
 18. Themethod of claim 17, wherein the cellular interaction is selected fromthe group consisting of heterologous cellular interaction, homologouscellular interaction, and combinations thereof.
 19. The method of claim1, wherein the dynamic behavior is selected from the group consisting ofmotility, cell cytotoxicity, cell death, protein secretion, cellularinteraction, and combinations thereof.
 20. The method of claim 1,wherein the assaying occurs by visualizing the dynamic behavior.
 21. Themethod of claim 20, wherein the visualizing occurs by a method selectedfrom the group consisting of microscopy, time-lapse imaging microscopy,fluorescence microscopy, multi-photon microscopy, quantitative phasemicroscopy, surface enhanced Raman spectroscopy, videography, manualvisual analysis, automated visual analysis, and combinations thereof.22. The method of claim 20, wherein the visualizing occurs by time-lapseimaging microscopy.
 23. The method of claim 1, wherein the assayingcomprises labeling the cell population.
 24. The method of claim 23,wherein the cell population is labeled by staining cells withfluorescent-based detection reagents.
 25. The method of claim 1, whereinthe assaying occurs automatically.
 26. The method of claim 25, whereinthe assaying occurs automatically through the use of algorithms.
 27. Themethod of claim 1, wherein the assaying comprises quantification of thedynamic behavior.
 28. The method of claim 1, wherein the assaying occursat a single-cell level.
 29. The method of claim 1, wherein the dynamicbehavior comprises motility, and wherein the motility is assayed byevaluating at least one of cellular location, cellular movements,cellular displacement, cellular speed, cellular movement paths on thearea, cellular infiltration, cellular trafficking, and combinationsthereof.
 30. The method of claim 1, wherein the dynamic behaviorcomprises cell death, and wherein the cell death is evaluated bydetecting apoptosis markers.
 31. The method of claim 1, wherein thedynamic behavior comprises cell cytotoxicity, and wherein the cellcytotoxicity is assayed by evaluating release of cytotoxic moleculesfrom the cell population.
 32. The method of claim 1, wherein the dynamicbehavior comprises cellular interaction, and wherein the cellularinteraction is assayed by evaluating duration of cellular interactions,number of cellular interactions, calcium activation, granulepolarization, protein localization, motility during cellularinteraction, termination of cellular interaction, and combinationsthereof.
 33. The method of claim 1, wherein the dynamic behaviorcomprises cell death and cellular interaction, and wherein the dynamicbehavior is assayed by evaluating at least one of time between firstcellular contact and death, the number of cellular contacts prior tocell death, cumulative duration of cellular interaction between firstcellular contact and target cell death (t_(Contact)), time between firstcellular contact and target cell death (t_(Death)), time betweentermination of cellular contact and target cell death, number of celldeaths caused by an individual cell, and combinations thereof.
 34. Themethod of claim 1, wherein the assaying comprises the use of a sensorassociated with the area.
 35. The method of claim 34, wherein the sensorcomprises an analyte binding agent.
 36. The method of claim 35, whereinthe analyte binding agent is selected from the group consisting ofgenes, nucleotide sequences, interference RNA (RNAi), antisenseoligonucleotides, peptides, antisense peptides, antigene peptide nucleicacids (PNA), proteins, antibodies, and combinations thereof.
 37. Themethod of claim 36, wherein the analyte binding agent is directedagainst an analyte of interest.
 38. The method of claim 37, wherein theanalyte of interest is selected from the group consisting of secretedproteins, cell lysate components, cellular receptors, metabolites,lipids, microvesicles, exosomes, microparticles, small molecules,protons, carbohydrates, and combinations thereof.
 39. The method ofclaim 37, wherein the analyte of interest is captured by the sensors,and wherein the analyte of interest is subsequently characterized. 40.The method of claim 39, wherein the analyte of interest is characterizedby methods selected from the group consisting of mass spectrometry,sequencing, microscopy, nucleic acid hybridization, immunoassay-baseddetection, and combinations thereof.
 41. The method of claim 34, whereinthe sensor is in the form of a bead.
 42. The method of claim 41, whereinthe bead comprises diameters that range from about 1 μm to about 10 μm.43. The method of claim 41, wherein the bead comprises diameters thatrange from about 3 μm to about 5 μm.
 44. The method of claim 34, whereinthe sensor is utilized to assay the dynamic behavior of a single cell inthe cell population in real-time.
 45. The method of claim 34, whereinthe dynamic behavior is selected from the group consisting of cellularactivation, cellular inhibition, protein secretion, microvesiclesecretion, exosome secretion, microparticle secretion, metabolitesecretion, small molecule secretion, proton secretion, proteinexpression, and combinations thereof.
 46. The method of claim 34,wherein the dynamic behavior comprises protein expression, and whereinthe protein expression is assayed by the sensor through capture of celllysate components.
 47. The method of claim 34, wherein the dynamicbehavior comprises protein secretion, and wherein the protein secretionis assayed by the sensor through capture of secreted proteins.
 48. Themethod of claim 34, wherein the sensor is utilized as a fiduciary markerto enable auto-focusing of the cell population during the assaying. 49.The method of claim 34, wherein the cell population is lysed prior toincubation with the sensors.
 50. The method of claim 1, wherein theassaying occurs at sequential intervals for a period of time.
 51. Themethod of claim 50, wherein the period of time ranges from about 1 hourto about 24 hours.
 52. The method of claim 50, wherein the sequentialintervals range from about 1 minute to about 10 minutes.
 53. The methodof claim 1, wherein the one or more cells are identified automaticallythrough the use of algorithms.
 54. The method of claim 1, wherein thecharacterized molecular profile of the one or more identified cells isselected from the group consisting of transcription activity,transcriptomic profile, gene expression activity, genomic profile,protein expression activity, proteomic profile, protein interactionactivity, cellular receptor expression activity, lipid profile, lipidactivity, carbohydrate profile, microvesicle activity, glucose activity,metabolic profile, and combinations thereof.
 55. The method of claim 1,wherein the characterizing occurs by a method selected from the groupconsisting of DNA analysis, RNA analysis, protein analysis, lipidanalysis, metabolite analysis, mass spectrometry, and combinationsthereof.
 56. The method of claim 1, wherein the characterizing occurs byRNA or DNA sequencing.
 57. The method of claim 56, wherein the RNA orDNA sequencing occurs by methods selected from the group consisting ofwhole transcriptome analysis, whole genome analysis, barcoded sequencingof whole or targeted regions of the genome, and combinations thereof.58. The method of claim 1, wherein the characterizing occurs by proteinanalysis.
 59. The method of claim 58, wherein the protein analysisoccurs at the proteomic level by multiplexed fluorescent staining. 60.The method of claim 1, wherein the correlating comprises integrating theassayed dynamic behavior and the characterized molecular profile. 61.The method of claim 1, wherein the correlating comprises correlating themotility of the one or more identified cells to gene expression ortranscription activities of the one or more identified cells.
 62. Themethod of claim 1, wherein the correlating comprises correlating themotility of the one or more identified cells to protein interactionactivity of the one or more identified cells.
 63. The method of claim 1,wherein the correlating comprises correlating the cellular interactionactivity of the one or more identified cells to protein expressionactivity of the one or more identified cells.
 64. The method of claim 1,wherein the method is utilized for at least one of predicting clinicaloutcome of a treatment, screening cells, retrieving cells, facilitatinga treatment, diagnosing a disease, monitoring cellular activity, andcombinations thereof.
 65. The method of claim 1, wherein the method isutilized to facilitate a treatment, and wherein the treatment comprisesimmunotherapy.
 66. The method of claim 1, wherein the method is utilizedto monitor cellular activity, and wherein the cellular activitycomprises an immune response.
 67. The method of claim 1, wherein themethod is utilized for screening of cells, and wherein the cellscomprise multi-killer T cells.
 68. A method of evaluating cellularactivity, said method comprising: (a) placing a cell population on anarea, wherein the area is associated with a sensor; and (b) assaying fora dynamic behavior of the cell population as a function of time.
 69. Themethod of claim 68, wherein the sensor is immobilized on the area. 70.The method of claim 68, wherein the sensor comprises an analyte bindingagent.
 71. The method of claim 70, wherein the analyte binding agent isselected from the group consisting of genes, nucleotide sequences,interference RNA (RNAi), antisense oligonucleotides, peptides, antisensepeptides, antigene peptide nucleic acids (PNA), proteins, antibodies,and combinations thereof.
 72. The method of claim 70, wherein theanalyte binding agent is directed against an analyte of interest. 73.The method of claim 72, wherein the analyte of interest is selected fromthe group consisting of secreted proteins, cell lysate components,cellular receptors, metabolites, lipids, microvesicles, exosomes,microparticles, small molecules, protons, carbohydrates, andcombinations thereof.
 74. The method of claim 72, wherein the analyte ofinterest is captured by the sensors, and wherein the analyte of interestis characterized.
 75. The method of claim 74, wherein the analyte ofinterest is characterized by methods selected from the group consistingof mass spectrometry, sequencing, microscopy, nucleic acidhybridization, immunoassay-based detection, and combinations thereof.76. The method of claim 68, wherein the sensor is in the form of a bead.77. The method of claim 76, wherein the bead comprises diameters thatrange from about 1 μm to about 10 μm.
 78. The method of claim 76,wherein the bead comprises diameters that range from about 3 μm to about5 μm.
 79. The method of claim 68, wherein the sensor is utilized toassay the dynamic behavior of the cell population in real-time.
 80. Themethod of claim 68, wherein the sensor is utilized to assay the dynamicbehavior of a single cell in the cell population in real-time.
 81. Themethod of claim 68, wherein the dynamic behavior is selected from thegroup consisting of cellular activation, cellular inhibition, cellularinteraction, protein expression, protein secretion, microvesiclesecretion, exosome secretion, microparticle secretion, metabolitesecretion, small molecule secretion, proton secretion, cellularproliferation, changes in cellular morphology, motility, cell death,cell cytotoxicity, cell lysis, cell membrane polarization, establishmentof a synapse, dynamic trafficking of proteins, granule polarization,calcium activation, metabolic changes, metabolite secretion, lipidchanges, microvesicle secretion, exosome secretion, microparticlesecretion, changes in cellular mass, and combinations thereof.
 82. Themethod of claim 68, wherein the assaying occurs by visualizing thedynamic behavior.
 83. The method of claim 82, wherein the visualizingoccurs by a method selected from the group consisting of microscopy,time-lapse imaging microscopy, fluorescence microscopy, multi-photonmicroscopy, quantitative phase microscopy, surface enhanced Ramanspectroscopy, videography, manual visual analysis, automated visualanalysis, and combinations thereof.
 84. The method of claim 68, whereinthe dynamic behavior comprises protein expression, and wherein theprotein expression is assayed by the sensor through capture of celllysate components.
 85. The method of claim 68, wherein the dynamicbehavior comprises protein secretion, and wherein the protein secretionis assayed by the sensor through capture of secreted proteins.
 86. Themethod of claim 68, wherein the sensor is utilized as a fiduciary markerto enable auto-focusing of the cell population during the assaying. 87.The method of claim 68, wherein the cell population is incubated withthe sensors.
 88. The method of claim 68, wherein the cell population islysed prior to incubation with the sensors.
 89. The method of claim 68,wherein the cell population comprises cells selected from the groupconsisting of plant cells, fungi cells, bacterial cells, prokaryoticcells, eukaryotic cells, unicellular cells, multi-cellular cells, immunecells, and combinations thereof.
 90. The method of claim 68, wherein thecell population comprises immune cells.
 91. The method of claim 68,wherein the cell population comprises cells selected from the groupconsisting of T cells, B cells, monocytes, macrophages, neutrophils,dendritic cells, natural killer cells, fibroblasts, stromal cells, stemcells, progenitor cells, tumor cells, tumor stem cells, tumorinfiltrating lymphocytes, and combinations thereof.
 92. The method ofclaim 68, wherein the cell population comprises T cells.
 93. The methodof claim 92, wherein the T cells are selected from the group consistingof helper T cells, cytotoxic T cells, natural killer T cells,genetically modified T cells, chimeric antigen receptor (CAR) modified Tcells, and combinations thereof.